Pub Date : 2013-07-15DOI: 10.1109/CEC.2013.6557676
Abel García-Nájera, M. Gutiérrez-Ándrade
The pickup and delivery problem (PDP) has many real-life applications. In this problem there is a customer set which is partitioned into two subsets: the customers requiring an amount of product (delivery) and the customers providing the product (pickup). There is also a set of transportation requests, which specify the quantity of product that has to be picked up from an origin customer and delivered to a destination customer. There exist a number of vehicles available to be used for completing these tasks. PDP consists of finding a collection of routes with minimum cost, such that all transportation request are serviced. Traditionally, the number of routes has been minimized first, and then the travel distance, however, if these objectives are considered to be equally important, the problem can be tackled as a bi-objective problem. Moreover, time is not always directly proportional to distance, thus travel time can also be considered an important criterion to be optimized and, consequently, PDP has to be regarded as a tri-objective problem. In this paper, we solve PDP as a problem with multiple objectives by means of an evolutionary algorithm and evaluate its performance with proper multi-objective performance tools.
{"title":"An evolutionary approach to the multi-objective pickup and delivery problem with time windows","authors":"Abel García-Nájera, M. Gutiérrez-Ándrade","doi":"10.1109/CEC.2013.6557676","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557676","url":null,"abstract":"The pickup and delivery problem (PDP) has many real-life applications. In this problem there is a customer set which is partitioned into two subsets: the customers requiring an amount of product (delivery) and the customers providing the product (pickup). There is also a set of transportation requests, which specify the quantity of product that has to be picked up from an origin customer and delivered to a destination customer. There exist a number of vehicles available to be used for completing these tasks. PDP consists of finding a collection of routes with minimum cost, such that all transportation request are serviced. Traditionally, the number of routes has been minimized first, and then the travel distance, however, if these objectives are considered to be equally important, the problem can be tackled as a bi-objective problem. Moreover, time is not always directly proportional to distance, thus travel time can also be considered an important criterion to be optimized and, consequently, PDP has to be regarded as a tri-objective problem. In this paper, we solve PDP as a problem with multiple objectives by means of an evolutionary algorithm and evaluate its performance with proper multi-objective performance tools.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116652259","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 : 2013-07-15DOI: 10.1109/CEC.2013.6557972
M. Riff, Elizabeth Montero
The process of designing a metaheuristic is a difficult and time consuming task as it usually requires tuning to find the best associated parameter values. In this paper, we propose a simple tuning tool called EVOCA which allows unexperimented metaheuristic designers to obtain good quality results without have a strong knowledge in tuning methods. The simplicity here means that the designer does not have to care about the initial settings of the tuner. We apply EVOCA to a genetic algorithm that solves NK landscape instances of various categories. We show that EVOCA is able to tune both categorical and numerical parameters allowing the designer to discard ineffective components for the algorithm.
{"title":"A new algorithm for reducing metaheuristic design effort","authors":"M. Riff, Elizabeth Montero","doi":"10.1109/CEC.2013.6557972","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557972","url":null,"abstract":"The process of designing a metaheuristic is a difficult and time consuming task as it usually requires tuning to find the best associated parameter values. In this paper, we propose a simple tuning tool called EVOCA which allows unexperimented metaheuristic designers to obtain good quality results without have a strong knowledge in tuning methods. The simplicity here means that the designer does not have to care about the initial settings of the tuner. We apply EVOCA to a genetic algorithm that solves NK landscape instances of various categories. We show that EVOCA is able to tune both categorical and numerical parameters allowing the designer to discard ineffective components for the algorithm.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123150039","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 : 2013-07-15DOI: 10.1109/CEC.2013.6557740
A. D. Campos, A. Pozo, E. P. Duarte
In this work we evaluate the application of multiple independent swarms to solve Many-Objective Problems (MaOPs). Solving MaOPs is often a challenge, as these problems do not have a single best solution, but a set of solutions. Furthermore, the objectives to be optimized are usually conflicting among themselves. Employing multiple independent swarms that evolve independently from each other is an effective optimization strategy, that pushes convergence while preserving the diversity of the solutions. One of the key decisions for organizing a set of swarms is to define the communication strategy they use to share solutions. The strategy defines how particles migrate among the swarms, and how much interaction they feature among themselves. We evaluate two multi-swarm communication strategies, broadcast and the probabilistic gossip to 1-neighbor. Extensive simulation results are presented for two members of the DTLZ family with 2, 3, 4, 5, 10, 15, and 20 objectives. A set of quality indicators were evaluated for both communication strategies as well as for a baseline reference execution based on a single swarm. Results show that both distributed strategies outperform the centralized alternative. It is also possible to conclude that the higher level of interactivity of the broadcast alternative proved to be the best for several scenarios.
{"title":"Evaluation of gossip Vs. broadcast as communication strategies for multiple swarms solving MaOPs","authors":"A. D. Campos, A. Pozo, E. P. Duarte","doi":"10.1109/CEC.2013.6557740","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557740","url":null,"abstract":"In this work we evaluate the application of multiple independent swarms to solve Many-Objective Problems (MaOPs). Solving MaOPs is often a challenge, as these problems do not have a single best solution, but a set of solutions. Furthermore, the objectives to be optimized are usually conflicting among themselves. Employing multiple independent swarms that evolve independently from each other is an effective optimization strategy, that pushes convergence while preserving the diversity of the solutions. One of the key decisions for organizing a set of swarms is to define the communication strategy they use to share solutions. The strategy defines how particles migrate among the swarms, and how much interaction they feature among themselves. We evaluate two multi-swarm communication strategies, broadcast and the probabilistic gossip to 1-neighbor. Extensive simulation results are presented for two members of the DTLZ family with 2, 3, 4, 5, 10, 15, and 20 objectives. A set of quality indicators were evaluated for both communication strategies as well as for a baseline reference execution based on a single swarm. Results show that both distributed strategies outperform the centralized alternative. It is also possible to conclude that the higher level of interactivity of the broadcast alternative proved to be the best for several scenarios.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128423814","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 : 2013-07-15DOI: 10.1109/CEC.2013.6557816
Eunice López-Camacho, H. Terashima-Marín
This paper presents an evolutionary framework that solves the one and two dimensional bin packing problem by combining several heuristics. The idea is to apply the heuristic that is more suitable at each stage of the solving process. To select a heuristic to apply, we characterize the problem employing a number of features. It is common in many existing approaches, that the user selects a set of features to represent the problem instances. In our solution model, we start with a large set of features, and those that succeed characterizing the instances are automatically selected during the evolutionary process. After providing a list of features, the user does not have to select the features that are best suitable to characterize problem instances. Therefore our system is more knowledge independent than previous approaches. This model produces better results employing the proposed feature selection approach compared against the use of other feature selection methodology.
{"title":"Evolving feature selection for characterizing and solving the 1D and 2D bin packing problem","authors":"Eunice López-Camacho, H. Terashima-Marín","doi":"10.1109/CEC.2013.6557816","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557816","url":null,"abstract":"This paper presents an evolutionary framework that solves the one and two dimensional bin packing problem by combining several heuristics. The idea is to apply the heuristic that is more suitable at each stage of the solving process. To select a heuristic to apply, we characterize the problem employing a number of features. It is common in many existing approaches, that the user selects a set of features to represent the problem instances. In our solution model, we start with a large set of features, and those that succeed characterizing the instances are automatically selected during the evolutionary process. After providing a list of features, the user does not have to select the features that are best suitable to characterize problem instances. Therefore our system is more knowledge independent than previous approaches. This model produces better results employing the proposed feature selection approach compared against the use of other feature selection methodology.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133231541","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 : 2013-07-15DOI: 10.1109/CEC.2013.6557785
Hiroyuki Hirano, T. Yoshikawa
Particle Swarm Optimization (PSO) is one of the most effective search methods in optimization problems. Multiobjective Optimization Problems (MOPs) has been focused on and PSO researches applied to MOPs have been reported. On the other hand, the problem that the search performance using conventional methods for MOPs becomes low is reported in Many-objective Optimization Problems (MaOPs) which have four or more objective functions. The authors have proposed two-step search method based on PSO for MaOPs. In the first step, it divides the population into some groups, and each group performs the single objective search for each objective function and the center of them. In the second step, the search is performed to acquire the diversity of Pareto solutions by PSO search with the goal, global-best, based on the solutions acquired in the first step. This paper defines the real coded multi-objective knapsack problem and studies the performance of the proposed method applied to this problem. The experimental results shows that the search of the first step for high convergence and that of the second step for large diversity aimed in the proposed method works well. It also shows that the proposed method is superior to other conventional methods especially in terms of the convergence in MaOPs.
{"title":"A study on two-step search based on PSO to improve convergence and diversity for Many-Objective Optimization Problems","authors":"Hiroyuki Hirano, T. Yoshikawa","doi":"10.1109/CEC.2013.6557785","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557785","url":null,"abstract":"Particle Swarm Optimization (PSO) is one of the most effective search methods in optimization problems. Multiobjective Optimization Problems (MOPs) has been focused on and PSO researches applied to MOPs have been reported. On the other hand, the problem that the search performance using conventional methods for MOPs becomes low is reported in Many-objective Optimization Problems (MaOPs) which have four or more objective functions. The authors have proposed two-step search method based on PSO for MaOPs. In the first step, it divides the population into some groups, and each group performs the single objective search for each objective function and the center of them. In the second step, the search is performed to acquire the diversity of Pareto solutions by PSO search with the goal, global-best, based on the solutions acquired in the first step. This paper defines the real coded multi-objective knapsack problem and studies the performance of the proposed method applied to this problem. The experimental results shows that the search of the first step for high convergence and that of the second step for large diversity aimed in the proposed method works well. It also shows that the proposed method is superior to other conventional methods especially in terms of the convergence in MaOPs.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115480004","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 : 2013-07-15DOI: 10.1109/CEC.2013.6557912
A. Mohammadi, M. Omidvar, Xiaodong Li
In this paper, we propose a metric for evaluating the performance of user-preference based evolutionary multiobjective algorithms by defining a preferred region based on the location of a user-supplied reference point. This metric uses a composite front which is a type of reference set and is used as a replacement for the Pareto-optimal front. This composite front is constructed by extracting the non-dominated solutions from the merged solution sets of all algorithms that are to be compared. A preferred region is then defined on the composite front based on the location of a reference point. Once the preferred region is defined, existing evolutionary multi-objective performance metrics can be applied with respect to the preferred region. In this paper the performance of a cardinality-based metric, a distance-based metric, and a volume-based metric are compared against a baseline which relies on knowledge of the Pareto-optimal front. The experimental results show that the distance-based and the volume-based metrics are consistent with the baseline, showing meaningful comparisons. However, the cardinality-based approach shows some inconsistencies and is not suitable for comparing the algorithms.
{"title":"A new performance metric for user-preference based multi-objective evolutionary algorithms","authors":"A. Mohammadi, M. Omidvar, Xiaodong Li","doi":"10.1109/CEC.2013.6557912","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557912","url":null,"abstract":"In this paper, we propose a metric for evaluating the performance of user-preference based evolutionary multiobjective algorithms by defining a preferred region based on the location of a user-supplied reference point. This metric uses a composite front which is a type of reference set and is used as a replacement for the Pareto-optimal front. This composite front is constructed by extracting the non-dominated solutions from the merged solution sets of all algorithms that are to be compared. A preferred region is then defined on the composite front based on the location of a reference point. Once the preferred region is defined, existing evolutionary multi-objective performance metrics can be applied with respect to the preferred region. In this paper the performance of a cardinality-based metric, a distance-based metric, and a volume-based metric are compared against a baseline which relies on knowledge of the Pareto-optimal front. The experimental results show that the distance-based and the volume-based metrics are consistent with the baseline, showing meaningful comparisons. However, the cardinality-based approach shows some inconsistencies and is not suitable for comparing the algorithms.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121379885","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557689
A. K. Qin, Xiaodong Li
Differential evolution (DE) is one of the most powerful continuous optimizers in the field of evolutionary computation. This work systematically benchmarks a classic DE algorithm (DE/rand/1/bin) on the CEC-2013 single-objective continuous optimization testbed. We report, for each test function at different problem dimensionality, the best achieved performance among a wide range of potentially effective parameter settings. It reflects the intrinsic optimization capability of DE/rand/1/bin on this testbed and can serve as a baseline for performance comparison in future research using this testbed. Furthermore, we conduct parameter sensitivity analysis using advanced non-parametric statistical tests to discover statistically significantly superior parameter settings. This analysis provides a statistically reliable rule of thumb for choosing the parameters of DE/rand/1/bin to solve unseen problems. Moreover, we report the performance of DE/rand/1/bin using one superior parameter setting advocated by parameter sensitivity analysis.
{"title":"Differential evolution on the CEC-2013 single-objective continuous optimization testbed","authors":"A. K. Qin, Xiaodong Li","doi":"10.1109/CEC.2013.6557689","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557689","url":null,"abstract":"Differential evolution (DE) is one of the most powerful continuous optimizers in the field of evolutionary computation. This work systematically benchmarks a classic DE algorithm (DE/rand/1/bin) on the CEC-2013 single-objective continuous optimization testbed. We report, for each test function at different problem dimensionality, the best achieved performance among a wide range of potentially effective parameter settings. It reflects the intrinsic optimization capability of DE/rand/1/bin on this testbed and can serve as a baseline for performance comparison in future research using this testbed. Furthermore, we conduct parameter sensitivity analysis using advanced non-parametric statistical tests to discover statistically significantly superior parameter settings. This analysis provides a statistically reliable rule of thumb for choosing the parameters of DE/rand/1/bin to solve unseen problems. Moreover, we report the performance of DE/rand/1/bin using one superior parameter setting advocated by parameter sensitivity analysis.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115414657","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557784
Markus Wagner, T. Friedrich
The Pareto front of a multi-objective optimization problem is typically very large and can only be approximated. Approximation-Guided Evolution (AGE) is a recently presented evolutionary multi-objective optimization algorithm that aims at minimizing iteratively the approximation factor, which measures how well the current population approximates the Pareto front. It outperforms state-of-the-art algorithms for problems with many objectives. However, AGE's performance is not competitive on problems with very few objectives. We study the reason for this behavior and observe that AGE selects parents uniformly at random, which has a detrimental effect on its performance. We then investigate different algorithm-specific selection strategies for AGE. The main difficulty here is finding a computationally efficient selection scheme which does not harm AGEs linear runtime in the number of objectives. We present several improved selections schemes that are computationally efficient and substantially improve AGE on low-dimensional objective spaces, but have no negative effect in high-dimensional objective spaces.
{"title":"Efficient parent selection for Approximation-Guided Evolutionary multi-objective optimization","authors":"Markus Wagner, T. Friedrich","doi":"10.1109/CEC.2013.6557784","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557784","url":null,"abstract":"The Pareto front of a multi-objective optimization problem is typically very large and can only be approximated. Approximation-Guided Evolution (AGE) is a recently presented evolutionary multi-objective optimization algorithm that aims at minimizing iteratively the approximation factor, which measures how well the current population approximates the Pareto front. It outperforms state-of-the-art algorithms for problems with many objectives. However, AGE's performance is not competitive on problems with very few objectives. We study the reason for this behavior and observe that AGE selects parents uniformly at random, which has a detrimental effect on its performance. We then investigate different algorithm-specific selection strategies for AGE. The main difficulty here is finding a computationally efficient selection scheme which does not harm AGEs linear runtime in the number of objectives. We present several improved selections schemes that are computationally efficient and substantially improve AGE on low-dimensional objective spaces, but have no negative effect in high-dimensional objective spaces.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115767759","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 : 2013-06-20DOI: 10.1109/CEC.2013.6557878
Masahiro Kanazaki, T. Matsuno, Kengo Maeda, H. Kawazoe
A Kriging-based genetic algorithm (GA) called efficient global optimization (EGO) was employed to optimize the parameters for the operating conditions of a plasma actuator (PA). The aerodynamic performance was evaluated by wind tunnel testing to overcome the disadvantages of time-consuming numerical simulations. The developed optimization system explores the optimum waveform of parameters for AC voltage by changing the waveform automatically. The proposed system was used on the drag minimization problem around a semicircular cylinder to design the power supply for a PA. Based on the results, the optimum design and global design information were obtained while drastically reducing the number of experiments required compared to a full factorial experiment.
{"title":"Wind tunnel evaluation-based optimization for improvement of flow control by plasma actuator using kriging model-based genetic algorithm","authors":"Masahiro Kanazaki, T. Matsuno, Kengo Maeda, H. Kawazoe","doi":"10.1109/CEC.2013.6557878","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557878","url":null,"abstract":"A Kriging-based genetic algorithm (GA) called efficient global optimization (EGO) was employed to optimize the parameters for the operating conditions of a plasma actuator (PA). The aerodynamic performance was evaluated by wind tunnel testing to overcome the disadvantages of time-consuming numerical simulations. The developed optimization system explores the optimum waveform of parameters for AC voltage by changing the waveform automatically. The proposed system was used on the drag minimization problem around a semicircular cylinder to design the power supply for a PA. Based on the results, the optimum design and global design information were obtained while drastically reducing the number of experiments required compared to a full factorial experiment.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127380846","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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