Pub Date : 2022-07-18DOI: 10.1109/CEC55065.2022.9870409
Stephen Y. Chen, Antonio Bolufé-Röhler, James Montgomery, Dania Tamayo-Vera, T. Hendtlass
The rate of Successful Exploration is related to the proportion of search solutions from fitter attraction basins that are fitter than the current reference solution. A reference solution that moves closer to its local optimum (i.e. experiences exploitation) will reduce the proportion of these fitter solutions, and this can lead to decreased rates of Successful Exploration/increased rates of Failed Exploration. This effect of Fitness-Based Selection is studied in Particle Swarm Optimization and Differential Evolution with increasing dimensionality of the search space. It is shown that increasing rates of Failed Exploration represent another aspect of the Curse of Dimensionality that needs to be addressed by metaheuristic design.
{"title":"Measuring the Effects of Increasing Dimensionality on Fitness-Based Selection and Failed Exploration","authors":"Stephen Y. Chen, Antonio Bolufé-Röhler, James Montgomery, Dania Tamayo-Vera, T. Hendtlass","doi":"10.1109/CEC55065.2022.9870409","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870409","url":null,"abstract":"The rate of Successful Exploration is related to the proportion of search solutions from fitter attraction basins that are fitter than the current reference solution. A reference solution that moves closer to its local optimum (i.e. experiences exploitation) will reduce the proportion of these fitter solutions, and this can lead to decreased rates of Successful Exploration/increased rates of Failed Exploration. This effect of Fitness-Based Selection is studied in Particle Swarm Optimization and Differential Evolution with increasing dimensionality of the search space. It is shown that increasing rates of Failed Exploration represent another aspect of the Curse of Dimensionality that needs to be addressed by metaheuristic design.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"246 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":"115650074","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/CEC55065.2022.9870321
I. Morais, Gabriel Souto, G. Ribeiro, Israel Mendonça, P. H. González
This paper presents a hybrid BRKGA (MP-HBRKGA), that combines BRKGA with a Local Branching technique, to solve the multiproduct two-stage capacitated facility location problem (MP-TSCFLP). In this problem, a set of different products has to be transported from a set of factories, passing through a set of depots (first stage) and then transported to a set of customers (second stage). The goal in the MP-TSCFLP is to minimize the opening and transportation costs, where each kind of product has its own transportation cost per unit transported. Recent hybrid methods have been successfully applied to facility location problems, therefore, in this paper we propose adaptations of such hybrid methods and implement the MP-HBRKGA for handling the multiproduct characteristic. To the best of our knowledge, such hybrid BRKGA presented the best results for the single-product problem and have not yet been applied to solve the problem with multiple products. Computational experiments compare the obtained results to those in the literature, using four sets, with different characteristics, of large-sized instances, proposed in the literature.
{"title":"A Hybrid BRKGA Approach for the Multiproduct Two Stage Capacitated Facility Location Problem","authors":"I. Morais, Gabriel Souto, G. Ribeiro, Israel Mendonça, P. H. González","doi":"10.1109/CEC55065.2022.9870321","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870321","url":null,"abstract":"This paper presents a hybrid BRKGA (MP-HBRKGA), that combines BRKGA with a Local Branching technique, to solve the multiproduct two-stage capacitated facility location problem (MP-TSCFLP). In this problem, a set of different products has to be transported from a set of factories, passing through a set of depots (first stage) and then transported to a set of customers (second stage). The goal in the MP-TSCFLP is to minimize the opening and transportation costs, where each kind of product has its own transportation cost per unit transported. Recent hybrid methods have been successfully applied to facility location problems, therefore, in this paper we propose adaptations of such hybrid methods and implement the MP-HBRKGA for handling the multiproduct characteristic. To the best of our knowledge, such hybrid BRKGA presented the best results for the single-product problem and have not yet been applied to solve the problem with multiple products. Computational experiments compare the obtained results to those in the literature, using four sets, with different characteristics, of large-sized instances, proposed in the literature.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"32 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":"121275994","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/CEC55065.2022.9870433
P. Bujok, Patrik Kolenovsky
In this paper, a cooperative model of four well-performing evolutionary algorithms enhanced by Eigen crossover is proposed and applied to a set of problems CEC 2022. The four adaptive algorithms employed in this model are - Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Differen-tial Evolution with Covariance Matrix Learning and Bimodal Distribution Parameter Setting (CoBiDE), an adaptive variant of jSO, and Differential Evolution With an Individual-Dependent Mechanism (IDE). For the higher efficiency of the cooperative model, a linear population-size reduction mechanism is employed. The model was introduced for CEC 2019. Here, Eigen crossover is applied for each cooperating algorithm. The provided results show that the proposed model of four Evolutionary Algorithms with Eigen crossover (EA4eig) is able to solve ten out of 24 optimisation problems. Moreover, comparing EA4eig with four state-of-the-art variants of adaptive Differential Evolution illustrates the superiority of the newly designed optimiser.
{"title":"Eigen Crossover in Cooperative Model of Evolutionary Algorithms Applied to CEC 2022 Single Objective Numerical Optimisation","authors":"P. Bujok, Patrik Kolenovsky","doi":"10.1109/CEC55065.2022.9870433","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870433","url":null,"abstract":"In this paper, a cooperative model of four well-performing evolutionary algorithms enhanced by Eigen crossover is proposed and applied to a set of problems CEC 2022. The four adaptive algorithms employed in this model are - Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Differen-tial Evolution with Covariance Matrix Learning and Bimodal Distribution Parameter Setting (CoBiDE), an adaptive variant of jSO, and Differential Evolution With an Individual-Dependent Mechanism (IDE). For the higher efficiency of the cooperative model, a linear population-size reduction mechanism is employed. The model was introduced for CEC 2019. Here, Eigen crossover is applied for each cooperating algorithm. The provided results show that the proposed model of four Evolutionary Algorithms with Eigen crossover (EA4eig) is able to solve ten out of 24 optimisation problems. Moreover, comparing EA4eig with four state-of-the-art variants of adaptive Differential Evolution illustrates the superiority of the newly designed optimiser.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"19 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":"121378877","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/CEC55065.2022.9870320
Ismail M. Ali, H. Turan, S. Elsawah
Differential evolution has a long track record of successfully solving optimization problems in continuous domain due it its powerful Euclidean distance-based learning concept. Although this affects its suitability for solving several problems with permutation variables, several studies show that it can be applicable for effectively solving permutation-based problems. In this paper, an improved design of differential evolution is introduced to solve a military fleet modernization problem with discrete parameters. In this problem, several modernization oper-ations are required to transition a military force from an outdated fleet to a more modern one with the objective of maximizing the force's deployment at the minimum cost over a pre-determined planning period. The proposed differential evolution incorporates a new solution representation, a proposed repairing heuristic method, a modified mutation operator and mapping method for efficiently tackling the discrete characteristics of the targeted problem and is coupled with a simulation model to evaluate the fitness of the generated solutions. To judge its performance, the proposed algorithm has been implemented to solve a case study that addresses recent fleet modernization strategies of the Australian Army to recapitalize its forces over the next decade and in a continual process. The experimental results show that the proposed algorithm can provide more efficient fleet modernization schedules which are 29.32% and 51.43% better than those obtained by other two comparative algorithms.
{"title":"A Discrete Differential Evolution Algorithm for a Military Fleet Modernization Problem","authors":"Ismail M. Ali, H. Turan, S. Elsawah","doi":"10.1109/CEC55065.2022.9870320","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870320","url":null,"abstract":"Differential evolution has a long track record of successfully solving optimization problems in continuous domain due it its powerful Euclidean distance-based learning concept. Although this affects its suitability for solving several problems with permutation variables, several studies show that it can be applicable for effectively solving permutation-based problems. In this paper, an improved design of differential evolution is introduced to solve a military fleet modernization problem with discrete parameters. In this problem, several modernization oper-ations are required to transition a military force from an outdated fleet to a more modern one with the objective of maximizing the force's deployment at the minimum cost over a pre-determined planning period. The proposed differential evolution incorporates a new solution representation, a proposed repairing heuristic method, a modified mutation operator and mapping method for efficiently tackling the discrete characteristics of the targeted problem and is coupled with a simulation model to evaluate the fitness of the generated solutions. To judge its performance, the proposed algorithm has been implemented to solve a case study that addresses recent fleet modernization strategies of the Australian Army to recapitalize its forces over the next decade and in a continual process. The experimental results show that the proposed algorithm can provide more efficient fleet modernization schedules which are 29.32% and 51.43% better than those obtained by other two comparative algorithms.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"44 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":"127171851","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/CEC55065.2022.9870429
Rasa Khosrowshahli, S. Rahnamayan, Azam Asilian Bidgoli
In recent years, center-based sampling has demonstrated impressive results to enhance the efficiency and effectiveness of meta-heuristic algorithms. The strategy of the center-based sampling can be utilized at either or both the operation and/or population level. Despite the overall efficiency of the center-based sampling in population-based algorithms, utilization at operation level requires customizing the strategy for a specific algorithm which degrades the scheme's generalization. In this paper, we have proposed a population-level center-based sampling method which is operation independent and correspondingly can be embedded in any population-based optimization algorithm. In this study, we applied the proposed scheme for Differential Evolution (DE) algorithm to enhance the exploration and exploitation capabilities of the algorithm. We cluster candidate solutions and inject the centroid-based samples into the population to increase the overall quality of the population and thus decrease the risk of premature convergence and stagnation. By a high chance, the center-based samples are effectively generated in the promising regions of the search space. The proposed method has been benchmarked by employing CEC-2017 benchmark test suite on dimensions 30, 50, and 100. The results clearly indicate the superiority of the proposed scheme, and a detailed results analysis is provided.
{"title":"Clustering Center-based Differential Evolution","authors":"Rasa Khosrowshahli, S. Rahnamayan, Azam Asilian Bidgoli","doi":"10.1109/CEC55065.2022.9870429","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870429","url":null,"abstract":"In recent years, center-based sampling has demonstrated impressive results to enhance the efficiency and effectiveness of meta-heuristic algorithms. The strategy of the center-based sampling can be utilized at either or both the operation and/or population level. Despite the overall efficiency of the center-based sampling in population-based algorithms, utilization at operation level requires customizing the strategy for a specific algorithm which degrades the scheme's generalization. In this paper, we have proposed a population-level center-based sampling method which is operation independent and correspondingly can be embedded in any population-based optimization algorithm. In this study, we applied the proposed scheme for Differential Evolution (DE) algorithm to enhance the exploration and exploitation capabilities of the algorithm. We cluster candidate solutions and inject the centroid-based samples into the population to increase the overall quality of the population and thus decrease the risk of premature convergence and stagnation. By a high chance, the center-based samples are effectively generated in the promising regions of the search space. The proposed method has been benchmarked by employing CEC-2017 benchmark test suite on dimensions 30, 50, and 100. The results clearly indicate the superiority of the proposed scheme, and a detailed results analysis is provided.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"2677 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":"127515884","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/CEC55065.2022.9870383
Michalis Mavrovouniotis, Changhe Li, G. Ellinas, M. Polycarpou
Electric vehicle routing problems are challenging variations of the traditional vehicle routing problem which incorporate the possibility of electric vehicle (EV) recharging at any station, while satisfying the delivery demands of customers. This work addresses the recently formulated capacitated vehicle routing problem (E-CVRP) with variable energy consumption rate. In particular, the cargo weight, which is one of the main factors affecting the energy consumption rate of EVs, is considered (i.e., the heavier the EV the higher the rate). As a solution method, an ant colony optimization algorithm with a local search heuristic is developed. Experiments are conducted on a recently generated benchmark set of E-CVRP instances demonstrating that the performance of the proposed technique improves on the best known so far solutions.
{"title":"Solving the Electric Capacitated Vehicle Routing Problem with Cargo Weight","authors":"Michalis Mavrovouniotis, Changhe Li, G. Ellinas, M. Polycarpou","doi":"10.1109/CEC55065.2022.9870383","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870383","url":null,"abstract":"Electric vehicle routing problems are challenging variations of the traditional vehicle routing problem which incorporate the possibility of electric vehicle (EV) recharging at any station, while satisfying the delivery demands of customers. This work addresses the recently formulated capacitated vehicle routing problem (E-CVRP) with variable energy consumption rate. In particular, the cargo weight, which is one of the main factors affecting the energy consumption rate of EVs, is considered (i.e., the heavier the EV the higher the rate). As a solution method, an ant colony optimization algorithm with a local search heuristic is developed. Experiments are conducted on a recently generated benchmark set of E-CVRP instances demonstrating that the performance of the proposed technique improves on the best known so far solutions.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"39 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":"125135363","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/CEC55065.2022.9870435
P. Miranda, R. F. Mello, André C. A. Nascimento, Tapas Si
The sequencing of sampling algorithms has shown to be a promising approach in generating balanced versions of unbalanced data. Sequencing allows different algorithms of under-sampling and/or over-sampling to be performed in sequence, producing a resulting balanced database. However, defining the most appropriate sequence of sampling algorithms is challenging. This article treats the sequencing problem as a combinatorial optimization task and proposes a multi-objective optimization method to seek promising solutions that maximize the performance of classifiers both in accuracy and in F1-score. The results showed that the proposed method was capable of finding optimized sequences that improved the performance of the classifiers, obtaining statistically better results, mainly in F1- score, when compared with competing methods, in most of the selected unbalanced problems.
{"title":"Multi-Objective Optimization of Sampling Algorithms Pipeline for Unbalanced Problems","authors":"P. Miranda, R. F. Mello, André C. A. Nascimento, Tapas Si","doi":"10.1109/CEC55065.2022.9870435","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870435","url":null,"abstract":"The sequencing of sampling algorithms has shown to be a promising approach in generating balanced versions of unbalanced data. Sequencing allows different algorithms of under-sampling and/or over-sampling to be performed in sequence, producing a resulting balanced database. However, defining the most appropriate sequence of sampling algorithms is challenging. This article treats the sequencing problem as a combinatorial optimization task and proposes a multi-objective optimization method to seek promising solutions that maximize the performance of classifiers both in accuracy and in F1-score. The results showed that the proposed method was capable of finding optimized sequences that improved the performance of the classifiers, obtaining statistically better results, mainly in F1- score, when compared with competing methods, in most of the selected unbalanced problems.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"4 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":"126614394","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/CEC55065.2022.9870299
Pawel Jocko, B. Ombuki-Berman, A. Engelbrecht
This study conducts a sensitivity analysis of the recently proposed multi-guide particle swarm optimisation (MG-PSO) algorithm for dynamic multi-objective optimisation problems (DMOPs). The MGPSO is a multi-swarm approach where each subswarm optimises one of the objectives. This paper further adapts the MGPSO algorithm to solve DMOPs by proposing alternative balance coefficient update strategies to allow efficient tracking of the changing Pareto-optimal front (POF). A total of twenty-nine benchmark functions and six performance measures were implemented to help with this task. The experiments were run against five different environment types to determine whether the MGPSO can solve problems with various spatial and temporal severities. The best control parameter update strategy was then compared with other state-of-the-art dynamic multi-objective optimisation algorithms (DMOAs). An extensive empirical analysis shows that MGPSO with the balance coefficient parameter re-initialized after the environment change achieves very competitive and oftentimes better performance when compared with the competing algorithms.
本文对动态多目标优化问题(dops)的多导粒子群优化算法(MG-PSO)进行了灵敏度分析。MGPSO是一种多群体方法，其中每个子群体优化一个目标。本文进一步将MGPSO算法应用于dmpp求解，提出了可选的平衡系数更新策略，以实现对变化的Pareto-optimal front (POF)的有效跟踪。总共实现了29个基准函数和6个性能度量来帮助完成这项任务。在五种不同的环境类型下进行了实验，以确定MGPSO是否可以解决不同时空严重程度的问题。然后将最佳控制参数更新策略与其他最先进的动态多目标优化算法(DMOAs)进行比较。大量的实证分析表明，在环境变化后重新初始化平衡系数参数的MGPSO与竞争算法相比，具有很强的竞争力，并且往往具有更好的性能。
{"title":"Dynamic Multi-objective Optimisation Using Multi-guide Particle Swarm Optimisation","authors":"Pawel Jocko, B. Ombuki-Berman, A. Engelbrecht","doi":"10.1109/CEC55065.2022.9870299","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870299","url":null,"abstract":"This study conducts a sensitivity analysis of the recently proposed multi-guide particle swarm optimisation (MG-PSO) algorithm for dynamic multi-objective optimisation problems (DMOPs). The MGPSO is a multi-swarm approach where each subswarm optimises one of the objectives. This paper further adapts the MGPSO algorithm to solve DMOPs by proposing alternative balance coefficient update strategies to allow efficient tracking of the changing Pareto-optimal front (POF). A total of twenty-nine benchmark functions and six performance measures were implemented to help with this task. The experiments were run against five different environment types to determine whether the MGPSO can solve problems with various spatial and temporal severities. The best control parameter update strategy was then compared with other state-of-the-art dynamic multi-objective optimisation algorithms (DMOAs). An extensive empirical analysis shows that MGPSO with the balance coefficient parameter re-initialized after the environment change achieves very competitive and oftentimes better performance when compared with the competing algorithms.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"80 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":"115046550","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/CEC55065.2022.9870231
Ahmed Hassan, N. Pillay
The automated design of search techniques is a recent trend in artificial intelligence research. Unfortunately, the majority of the automated design approaches are developed using trial and error which fails to justify or at least explain why some design decisions succeed while others fail. This approach is a host of evils as it has resulted in poorly understood systems for poorly understood problems. This study is an attempt to improve our understanding of the automated design of hybrid metaheuristics by utilizing fitness landscape analysis to reveal the topological characteristics that can be exploited to design better automated approaches. We consider the sequential hybridization, including algorithm configuration and parameter tuning, of single-point and multi-point metaheuristics and three optimization problems which are the earth-observing satellite scheduling problem, the aircraft landing problem and the two-dimensional bin packing problem. Interestingly, the design space exhibits similar trends regardless of the underlining optimization problem. The design space is found to be rugged, multimodal, moderately searchable, has multiple funnels, and almost no plateau. Based on these findings, deeper insights are provided to guide the development of future automated approaches instead of blindly trying different options.
{"title":"Automated Design of Hybrid Metaheuristics: A Fitness Landscape Analysis","authors":"Ahmed Hassan, N. Pillay","doi":"10.1109/CEC55065.2022.9870231","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870231","url":null,"abstract":"The automated design of search techniques is a recent trend in artificial intelligence research. Unfortunately, the majority of the automated design approaches are developed using trial and error which fails to justify or at least explain why some design decisions succeed while others fail. This approach is a host of evils as it has resulted in poorly understood systems for poorly understood problems. This study is an attempt to improve our understanding of the automated design of hybrid metaheuristics by utilizing fitness landscape analysis to reveal the topological characteristics that can be exploited to design better automated approaches. We consider the sequential hybridization, including algorithm configuration and parameter tuning, of single-point and multi-point metaheuristics and three optimization problems which are the earth-observing satellite scheduling problem, the aircraft landing problem and the two-dimensional bin packing problem. Interestingly, the design space exhibits similar trends regardless of the underlining optimization problem. The design space is found to be rugged, multimodal, moderately searchable, has multiple funnels, and almost no plateau. Based on these findings, deeper insights are provided to guide the development of future automated approaches instead of blindly trying different options.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"115 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":"122886161","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/CEC55065.2022.9870290
Miguel Vieira, Ricardo Faia, F. Lezama, Z. Vale
Energy community markets have emerged to promote prosumers' active participation and empowerment in the electrical power system. These initiatives allow prosumers to transact electricity locally without an intermediary such as an aggregator. However, it is necessary to implement optimization methods that determine the best transactions within the energy community, obtaining the best solution under these models. Particle Swarm Optimization (PSO) fits this type of problem well because it allows reaching results in short optimization times. Furthermore, applying this metaheuristic to the problem is easy compared to other available optimization tools. In this work, we provide a sensitivity analysis of the impact of different parameters of PSO in solving an energy community market problem. As a result, the combination of parameters that lead to the best results is obtained, demonstrating the effectiveness of PSO solving different case studies.
{"title":"A Sensitivity Analysis of PSO Parameters Solving the P2P Electricity Market Problem","authors":"Miguel Vieira, Ricardo Faia, F. Lezama, Z. Vale","doi":"10.1109/CEC55065.2022.9870290","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870290","url":null,"abstract":"Energy community markets have emerged to promote prosumers' active participation and empowerment in the electrical power system. These initiatives allow prosumers to transact electricity locally without an intermediary such as an aggregator. However, it is necessary to implement optimization methods that determine the best transactions within the energy community, obtaining the best solution under these models. Particle Swarm Optimization (PSO) fits this type of problem well because it allows reaching results in short optimization times. Furthermore, applying this metaheuristic to the problem is easy compared to other available optimization tools. In this work, we provide a sensitivity analysis of the impact of different parameters of PSO in solving an energy community market problem. As a result, the combination of parameters that lead to the best results is obtained, demonstrating the effectiveness of PSO solving different case studies.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"45 2 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":"122904398","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：