Pub Date : 2022-07-18DOI: 10.1109/CEC55065.2022.9870266
Victoria Miranda-Burgos, Nicolás Rojas-Morales
The family of Knapsack Problems (KP) has been relevant in many works and studies as their use in modeling, simplifying complex problems or decision-making processes. Because of its importance, several metaheuristic algorithms have been designed or evaluated using this type of problem. In some variants of the KP, Tabu Search approaches are competitive or part of the state-of-the-art. This work proposes opposition-inspired strategies to improve the diversification of Tabu Search (TS) algorithms proposed for solving KPs. We use the well-known TSTS algorithm to evaluate our strategies, designed for solving the Multidemand Multidimensional Knapsack Problem. Results show that the usage of our opposite strategies allow the target algorithm to improve its performance in several benchmark instances.
{"title":"Opposition-Inspired Strategies for Tabu Search approaches proposed for Knapsack Problems","authors":"Victoria Miranda-Burgos, Nicolás Rojas-Morales","doi":"10.1109/CEC55065.2022.9870266","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870266","url":null,"abstract":"The family of Knapsack Problems (KP) has been relevant in many works and studies as their use in modeling, simplifying complex problems or decision-making processes. Because of its importance, several metaheuristic algorithms have been designed or evaluated using this type of problem. In some variants of the KP, Tabu Search approaches are competitive or part of the state-of-the-art. This work proposes opposition-inspired strategies to improve the diversification of Tabu Search (TS) algorithms proposed for solving KPs. We use the well-known TSTS algorithm to evaluate our strategies, designed for solving the Multidemand Multidimensional Knapsack Problem. Results show that the usage of our opposite strategies allow the target algorithm to improve its performance in several benchmark instances.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"277 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122165029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/CEC55065.2022.9870385
C. Marcelino, E. Wanner, F. V. Martins, J. Pérez-Aracil, S. Jiménez-Fernández, S. Salcedo-Sanz
Optimal active–reactive power dispatch problems (OARPD) are considered large scale optimization problems with a high nonlinear complexity. Usually, in OARPD the objective is to minimize the cost of the system operation. In 2018, the IEEE PES committee proposed a competition, the “Operational planning of sustainable power systems”, in which a test bed relating the OARPD and a renewable energy generation challenge within a smart grid was proposed. In this work we consider three test scenarios proposed in that competition. Specifically, we present a hybrid meta-heuristic optimization approach applied to the OARPD, the Canonical Differential Evolutionary Particle Swarm Optimization (C-DEEPSO), to tackle these test scenarios. Comparative results with other algorithms such as CMA-ES, EPSO, and CEEPSO indicate that C-DEEPSO shows a competitive performance when solving the OARPD problems.
{"title":"Solving the Optimal Active–Reactive Power Dispatch Problem in Smart Grids with the C-DEEPSO Algorithm","authors":"C. Marcelino, E. Wanner, F. V. Martins, J. Pérez-Aracil, S. Jiménez-Fernández, S. Salcedo-Sanz","doi":"10.1109/CEC55065.2022.9870385","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870385","url":null,"abstract":"Optimal active–reactive power dispatch problems (OARPD) are considered large scale optimization problems with a high nonlinear complexity. Usually, in OARPD the objective is to minimize the cost of the system operation. In 2018, the IEEE PES committee proposed a competition, the “Operational planning of sustainable power systems”, in which a test bed relating the OARPD and a renewable energy generation challenge within a smart grid was proposed. In this work we consider three test scenarios proposed in that competition. Specifically, we present a hybrid meta-heuristic optimization approach applied to the OARPD, the Canonical Differential Evolutionary Particle Swarm Optimization (C-DEEPSO), to tackle these test scenarios. Comparative results with other algorithms such as CMA-ES, EPSO, and CEEPSO indicate that C-DEEPSO shows a competitive performance when solving the OARPD problems.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129332559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/CEC55065.2022.9870227
Jing J. Liang, Junting Yang, C. Yue, Gongping Li, Kunjie Yu, B. Qu
When performing feature selection on most data sets, there is a general situation that some different feature subsets have the same number of selected features and classification error rate. This indicates that feature selection in some data sets is a multimodal multiobjective optimization (MMO) problem. Most of the current studies on feature selection ignore the MMO problems. Therefore, this paper proposes a feature selection method based on a multimodal multiobjective genetic algorithm (MMOGA) to solve the problem. This algorithm is mainly improved in three aspects. First, a special initialization strategy based on symmetric uncertainty is designed to improve the fitness of the initial population. Second, this paper adds a niche strategy to the genetic algorithm to search for multimodal solutions. Unlike traditional niche methods that has a central individual, this algorithm also considers the distances between individuals in the niche. Third, to effectively utilize excellent individuals for evolution, this algorithm uses a method based on the Pareto set of the niche to generate offspring. Finally, by comparing with other algorithms, the effectiveness of the MMOGA in feature selection is verified. This algorithm can successfully find equivalent feature subsets on different datasets.
{"title":"A Multimodal Multiobjective Genetic Algorithm for Feature Selection","authors":"Jing J. Liang, Junting Yang, C. Yue, Gongping Li, Kunjie Yu, B. Qu","doi":"10.1109/CEC55065.2022.9870227","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870227","url":null,"abstract":"When performing feature selection on most data sets, there is a general situation that some different feature subsets have the same number of selected features and classification error rate. This indicates that feature selection in some data sets is a multimodal multiobjective optimization (MMO) problem. Most of the current studies on feature selection ignore the MMO problems. Therefore, this paper proposes a feature selection method based on a multimodal multiobjective genetic algorithm (MMOGA) to solve the problem. This algorithm is mainly improved in three aspects. First, a special initialization strategy based on symmetric uncertainty is designed to improve the fitness of the initial population. Second, this paper adds a niche strategy to the genetic algorithm to search for multimodal solutions. Unlike traditional niche methods that has a central individual, this algorithm also considers the distances between individuals in the niche. Third, to effectively utilize excellent individuals for evolution, this algorithm uses a method based on the Pareto set of the niche to generate offspring. Finally, by comparing with other algorithms, the effectiveness of the MMOGA in feature selection is verified. This algorithm can successfully find equivalent feature subsets on different datasets.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127768094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/CEC55065.2022.9870317
Martina Saletta, C. Ferretti
Neural networks for source code processing have proven to be effective for solving multiple tasks, such as locating bugs or detecting vulnerabilities. In this paper, we propose an evolutionary approach for probing the behaviour of a deep neural source code classifier by generating instances that sample its input space. First, we apply a grammar-based genetic algorithm for evolving Python functions that minimise or maximise the probability of a function to be in a certain class, and we also produce programs that yield an output near to the classification threshold, namely for which the network does not express a clear classification preference. We then use such sets of evolved programs as initial popu-lations for an evolution strategy approach in which we apply, by following different policies, constrained small mutations to the individuals, so to both explore the decision boundary of the network and to identify the features that most contribute to a particular prediction. We furtherly point out how our approach can be effectively used for several tasks in the scope of the interpretable machine learning, such as for producing adversarial examples able to deceive a network, for identifying the most salient features, and further for characterising the abstract concepts learned by a neural model.
{"title":"A Grammar-based Evolutionary Approach for Assessing Deep Neural Source Code Classifiers","authors":"Martina Saletta, C. Ferretti","doi":"10.1109/CEC55065.2022.9870317","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870317","url":null,"abstract":"Neural networks for source code processing have proven to be effective for solving multiple tasks, such as locating bugs or detecting vulnerabilities. In this paper, we propose an evolutionary approach for probing the behaviour of a deep neural source code classifier by generating instances that sample its input space. First, we apply a grammar-based genetic algorithm for evolving Python functions that minimise or maximise the probability of a function to be in a certain class, and we also produce programs that yield an output near to the classification threshold, namely for which the network does not express a clear classification preference. We then use such sets of evolved programs as initial popu-lations for an evolution strategy approach in which we apply, by following different policies, constrained small mutations to the individuals, so to both explore the decision boundary of the network and to identify the features that most contribute to a particular prediction. We furtherly point out how our approach can be effectively used for several tasks in the scope of the interpretable machine learning, such as for producing adversarial examples able to deceive a network, for identifying the most salient features, and further for characterising the abstract concepts learned by a neural model.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127835081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/CEC55065.2022.9870393
Aitor Godoy, Ismael Rodríguez, F. Rubio
Reaching agreements is part of the life of any human group, but it is especially important in the context of political relations. In parliamentary systems, when no party has an absolute majority, it is necessary to establish pacts with other parties to carry out as many laws as possible that fit with our ideology. However, finding the best possible deals is not an easy task. In fact, in this work we not only show that it is an NP-complete problem, but also that it is impossible to guarantee a good approximation ratio in polynomial time. Even so, we show that it is possible to use genetic algorithms to obtain reasonably satisfactory pacts, and we illustrate it for a specific case study of the Spanish parliament.
{"title":"On the hardness of finding good pacts","authors":"Aitor Godoy, Ismael Rodríguez, F. Rubio","doi":"10.1109/CEC55065.2022.9870393","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870393","url":null,"abstract":"Reaching agreements is part of the life of any human group, but it is especially important in the context of political relations. In parliamentary systems, when no party has an absolute majority, it is necessary to establish pacts with other parties to carry out as many laws as possible that fit with our ideology. However, finding the best possible deals is not an easy task. In fact, in this work we not only show that it is an NP-complete problem, but also that it is impossible to guarantee a good approximation ratio in polynomial time. Even so, we show that it is possible to use genetic algorithms to obtain reasonably satisfactory pacts, and we illustrate it for a specific case study of the Spanish parliament.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134452364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/CEC55065.2022.9870400
Nina Bulanova, Arina Buzdalova, Carola Doerr
In real-world optimization scenarios, the problem instance that we are asked to solve may change during the optimization process, e.g., when new information becomes available or when the environmental conditions change. In such situations, one could hope to achieve reasonable performance by continuing the search from the best solution found for the original problem. Likewise, one may hope that when solving several problem instances that are similar to each other, it can be beneficial to “warm-start” the optimization process of the second instance by the best solution found for the first. However, it was shown in [Doerr et al., GECCO 2019] that even when initialized with structurally good solutions, evolutionary algorithms can have a tendency to replace these good solutions by structurally worse ones, resulting in optimization times that have no advantage over the same algorithms started from scratch. Doerr et al. also proposed a diversity mechanism to overcome this problem. Their approach balances greedy search around a best-so-far solution for the current problem with search in the neighborhood around the best-found solution for the previous instance. In this work, we first show that the re-optimization approach suggested by Doerr et al. reaches a limit when the problem instances are prone to more frequent changes. More precisely, we show that they get stuck on the dynamic LeadingOnes problem in which the target string changes periodically. We then propose a modification of their algorithm which interpolates between greedy search around the previous-best and the current-best solution. We empirically evaluate our smoothed re-optimization algorithm on LeadingOnes instances with various frequencies of change and with different perturbation factors and show that it outperforms both a fully restarted ($1+1$) Evolutionary Algorithm and the re-optimization approach by Doerr et al.
{"title":"Fast Re-Optimization of LeadingOnes with Frequent Changes","authors":"Nina Bulanova, Arina Buzdalova, Carola Doerr","doi":"10.1109/CEC55065.2022.9870400","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870400","url":null,"abstract":"In real-world optimization scenarios, the problem instance that we are asked to solve may change during the optimization process, e.g., when new information becomes available or when the environmental conditions change. In such situations, one could hope to achieve reasonable performance by continuing the search from the best solution found for the original problem. Likewise, one may hope that when solving several problem instances that are similar to each other, it can be beneficial to “warm-start” the optimization process of the second instance by the best solution found for the first. However, it was shown in [Doerr et al., GECCO 2019] that even when initialized with structurally good solutions, evolutionary algorithms can have a tendency to replace these good solutions by structurally worse ones, resulting in optimization times that have no advantage over the same algorithms started from scratch. Doerr et al. also proposed a diversity mechanism to overcome this problem. Their approach balances greedy search around a best-so-far solution for the current problem with search in the neighborhood around the best-found solution for the previous instance. In this work, we first show that the re-optimization approach suggested by Doerr et al. reaches a limit when the problem instances are prone to more frequent changes. More precisely, we show that they get stuck on the dynamic LeadingOnes problem in which the target string changes periodically. We then propose a modification of their algorithm which interpolates between greedy search around the previous-best and the current-best solution. We empirically evaluate our smoothed re-optimization algorithm on LeadingOnes instances with various frequencies of change and with different perturbation factors and show that it outperforms both a fully restarted ($1+1$) Evolutionary Algorithm and the re-optimization approach by Doerr et al.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133946048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/CEC55065.2022.9870350
Bing Wang, H. Singh, T. Ray
Bilevel optimization refers to a challenging class of problems where a lower level (LL) optimization task acts as a constraint for an upper level (UL) optimization task. When a bilevel problem is solved using a nested evolutionary algorithm (EA), a large number of function evaluations are consumed since an LL optimization needs to be conducted to evaluate every candidate UL solution. Knowledge transfer of optimal LL solutions between neighboring UL solutions is a plausible approach to improve the search efficiency. Even though some of the past studies have utilized this strategy intuitively, the specific impact of the transferred solution(s) has not been clearly differentiated since it forms only a small component of a much more elaborate search framework. In this study, we intend to examine closely the effectiveness of direct solution transfer. To do so, the transferred solution (LL optimum of the nearest UL solution) is considered as the mainstay of the LL search, acting as the starting point for a direct local LL search. We first observe the performance of this approach on existing benchmarks. Based on the understanding gained from the experiments, we design modified problems where such a direct transfer is likely to face significant challenges. We then propose an improved approach that uses solution transfer more selectively by considering correlations between neighboring landscapes for a more effective transfer. Numerical experiments are conducted to demonstrate the challenges faced by the direct transfer on the modified problems, as well as the competitive performance of the correlation-based approach. We hope that the insights gained from the study will be beneficial for future development of efficient transfer-based approaches for bilevel optimization.
{"title":"Investigating Neighborhood Solution Transfer Schemes for Bilevel Optimization","authors":"Bing Wang, H. Singh, T. Ray","doi":"10.1109/CEC55065.2022.9870350","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870350","url":null,"abstract":"Bilevel optimization refers to a challenging class of problems where a lower level (LL) optimization task acts as a constraint for an upper level (UL) optimization task. When a bilevel problem is solved using a nested evolutionary algorithm (EA), a large number of function evaluations are consumed since an LL optimization needs to be conducted to evaluate every candidate UL solution. Knowledge transfer of optimal LL solutions between neighboring UL solutions is a plausible approach to improve the search efficiency. Even though some of the past studies have utilized this strategy intuitively, the specific impact of the transferred solution(s) has not been clearly differentiated since it forms only a small component of a much more elaborate search framework. In this study, we intend to examine closely the effectiveness of direct solution transfer. To do so, the transferred solution (LL optimum of the nearest UL solution) is considered as the mainstay of the LL search, acting as the starting point for a direct local LL search. We first observe the performance of this approach on existing benchmarks. Based on the understanding gained from the experiments, we design modified problems where such a direct transfer is likely to face significant challenges. We then propose an improved approach that uses solution transfer more selectively by considering correlations between neighboring landscapes for a more effective transfer. Numerical experiments are conducted to demonstrate the challenges faced by the direct transfer on the modified problems, as well as the competitive performance of the correlation-based approach. We hope that the insights gained from the study will be beneficial for future development of efficient transfer-based approaches for bilevel optimization.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131655553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/CEC55065.2022.9870432
Iñigo Delgado-Enales, Patricia Molina-Costa, E. Osaba, Silvia Urra-Uriarte, J. Ser
Many countries around the world have witnessed the progressive ageing of their population, giving rise to a global concern to respond to the needs that this process will create. Besides the changes in the productive schemes and the evolution of the healthcare resources to new models, the accessibility of pedestrians belonging to this age range is grasping an increasing interest in urban planning processes. This work presents pre-liminary results of a framework that combines graph modeling and meta-heuristic optimization to inform decision makers in urban planning when deciding how to regenerate urban spaces taking into account pedestrian accessibility for the older people in urban areas with difficult orography. The goal of the framework is to decide where to deploy urban elements (mechanical ramps, escalators and lifts), so that an indirect measure of accessibility is improved while also accounting for the economical investment of the installation. We exploit the versatility of multi-objective evolutionary algorithms to tackle the underlying optimization problem. Experimental results of a case study located in the city of Santander (Spain) show that the proposed framework can support urban planners when making decisions regarding the accessibility of the public space.
{"title":"Improving the Urban Accessibility of Older Pedestrians using Multi-objective Optimization","authors":"Iñigo Delgado-Enales, Patricia Molina-Costa, E. Osaba, Silvia Urra-Uriarte, J. Ser","doi":"10.1109/CEC55065.2022.9870432","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870432","url":null,"abstract":"Many countries around the world have witnessed the progressive ageing of their population, giving rise to a global concern to respond to the needs that this process will create. Besides the changes in the productive schemes and the evolution of the healthcare resources to new models, the accessibility of pedestrians belonging to this age range is grasping an increasing interest in urban planning processes. This work presents pre-liminary results of a framework that combines graph modeling and meta-heuristic optimization to inform decision makers in urban planning when deciding how to regenerate urban spaces taking into account pedestrian accessibility for the older people in urban areas with difficult orography. The goal of the framework is to decide where to deploy urban elements (mechanical ramps, escalators and lifts), so that an indirect measure of accessibility is improved while also accounting for the economical investment of the installation. We exploit the versatility of multi-objective evolutionary algorithms to tackle the underlying optimization problem. Experimental results of a case study located in the city of Santander (Spain) show that the proposed framework can support urban planners when making decisions regarding the accessibility of the public space.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132797888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/CEC55065.2022.9870267
Weixi Chen, Huachao Dong, Peng Wang, Xiaozuo Liu
The blended-wing-body underwater glider (BWBUG) is a new type of underwater vehicle that has been applied in natural resource exploration with great success. Compared with conventional torpedo shapes, BWBUG's shape has a higher lift-to-drag ratio (LDR), so its shape design has become a research focus of ocean engineering in recent years. It is noteworthy that the traditional design process assumes no prior knowledge and starts from scratch. However, since problems rarely exist in isolation, solving the shape problem of a traditional glider may provide useful information, but the disparity in design space impedes information transmission. This paper presents a heterogeneous transfer optimization method for glider shape, which consists of four parts: simulation, image processing, manifold learning, and the evolution algorithm. The simulation's goal is to create pressure and velocity clouds. Manifold learning will use the information from cloud maps to create a low-dimensional feature space. The information mapped in low-dimensional space will be used to assist evolutionary algorithms in searching for optimal solutions. The proposed method was tested for the shape optimization problem of a BWBUG, and the results show that knowledge learned from different but related problem domains is potentially beneficial to the new design.
{"title":"Blended-wing-body underwater glider shape transfer optimization","authors":"Weixi Chen, Huachao Dong, Peng Wang, Xiaozuo Liu","doi":"10.1109/CEC55065.2022.9870267","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870267","url":null,"abstract":"The blended-wing-body underwater glider (BWBUG) is a new type of underwater vehicle that has been applied in natural resource exploration with great success. Compared with conventional torpedo shapes, BWBUG's shape has a higher lift-to-drag ratio (LDR), so its shape design has become a research focus of ocean engineering in recent years. It is noteworthy that the traditional design process assumes no prior knowledge and starts from scratch. However, since problems rarely exist in isolation, solving the shape problem of a traditional glider may provide useful information, but the disparity in design space impedes information transmission. This paper presents a heterogeneous transfer optimization method for glider shape, which consists of four parts: simulation, image processing, manifold learning, and the evolution algorithm. The simulation's goal is to create pressure and velocity clouds. Manifold learning will use the information from cloud maps to create a low-dimensional feature space. The information mapped in low-dimensional space will be used to assist evolutionary algorithms in searching for optimal solutions. The proposed method was tested for the shape optimization problem of a BWBUG, and the results show that knowledge learned from different but related problem domains is potentially beneficial to the new design.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127644056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/CEC55065.2022.9870296
Bin Li, Zhi–Bin Tang
Whereas the imperialist competitive algorithm (ICA) shows limited global search ability and be liable to be trapped into local optimum, a double-assimilation of prosperity and destruction oriented improved imperialist competitive algorithm (DPDO-IIC A) is proposed tentatively to overcome inherent defects. The imperialist assimilation and colonial reform strategy are customized purposefully, and a novel population redistribution mechanism is introduced as well. The three improvement measures are supposed to further promote population diversity and searching accuracy. The CEC2017 test set is selected to verify the performance of the DPDO-IICA by the different types of numerical function problems with the different dimensions. Moreover, the DPDO-IICA is compared with the three first-class intelligent optimization algorithms, which have achieved significant rankings in the CEC2017 competition. The comparison shows that the DPDO-IICA has good performances, which is demonstrated by the accuracy and stability. In addition, the proportion of imperialists and colonies is investigated, and it is through the community partitioning and clustering dynamically to enhance the population diversity. In conclusion, the DPDO-IICA can effectively improve the ability of global exploration and avoid premature convergence in comparison with the original ICA.
{"title":"Double-Assimilation of Prosperity and Destruction Oriented Improved Imperialist Competitive Algorithm with Computational Thinking","authors":"Bin Li, Zhi–Bin Tang","doi":"10.1109/CEC55065.2022.9870296","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870296","url":null,"abstract":"Whereas the imperialist competitive algorithm (ICA) shows limited global search ability and be liable to be trapped into local optimum, a double-assimilation of prosperity and destruction oriented improved imperialist competitive algorithm (DPDO-IIC A) is proposed tentatively to overcome inherent defects. The imperialist assimilation and colonial reform strategy are customized purposefully, and a novel population redistribution mechanism is introduced as well. The three improvement measures are supposed to further promote population diversity and searching accuracy. The CEC2017 test set is selected to verify the performance of the DPDO-IICA by the different types of numerical function problems with the different dimensions. Moreover, the DPDO-IICA is compared with the three first-class intelligent optimization algorithms, which have achieved significant rankings in the CEC2017 competition. The comparison shows that the DPDO-IICA has good performances, which is demonstrated by the accuracy and stability. In addition, the proportion of imperialists and colonies is investigated, and it is through the community partitioning and clustering dynamically to enhance the population diversity. In conclusion, the DPDO-IICA can effectively improve the ability of global exploration and avoid premature convergence in comparison with the original ICA.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133853988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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