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Genetic and Evolutionary Computation Conference (GECCO)

作者： Ishibuchi, Hisao Fukase, Takefumi Masuyama, Naoki Nojima, Yusuke Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Key Lab Computat Intelligence Shenzhen 518055 Peoples R China Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan

ISBN: (纸本)9781450356183

A promising idea for evolutionary constrained optimization is to efficiently utilize not only feasible solutions (feasible individuals) but also infeasible ones. In this paper, we propose a simple implementation of this idea in MOEA/D. In the proposed method, MOEA/D has two grids of weight vectors. One is used for maintaining the main population as in the standard MOEA/D. In the main population, feasible solutions always have higher fitness than infeasible ones. Among infeasible solutions, solutions with smaller constraint violations have higher fitness. The other grid is for maintaining a secondary population where non-dominated solutions with respect to scalarizing function values and constraint violations are stored. More specifically, a single non-dominated solution with respect to the scalarizing function and the total constraint violation is stored for each weight vector. A new solution is generated from a pair of neighboring solutions in the two grids. That is, there exist three possible combinations of two parents: both from the main population, both from the secondary population, and each from each population. The proposed MOEA/D variant is compared with the standard MOEA/D and other evolutionary algorithms for constrained multiobjective optimization through computational experiments.

关键词： Evolutionary multiobjective optimization (EMO) constrained multiobjective optimization decomposition-based multiobjective optimization algorithms MOEA/D

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Differential Evolution with the Adaptive Penalty Method for constrained multiobjective optimization

Differential Evolution with the Adaptive Penalty Method for ...

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IEEE Congress on Evolutionary Computation

作者： Vargas, Denis E. C. Lemonge, Afonso C. C. Barbosa, Helio J. C. Bernardino, Heder S. Univ Fed Juiz de Fora Juiz de Fora MG Brazil

ISBN: (纸本)9781479904549;9781479904532

A differential evolution algorithm is proposed here to solve constrained multiobjective optimization problems (CMOPs). In this paper, an Adaptive Penalty Method (APM), which was successfully applied to solve single objective optimization problems, is used to handle the constraints. That constraint handling technique is incorporated to a multiobjective DE which combines the non-dominated ranking and crowding distance schemes when the candidate solutions are replaced. Previously, several variants of the APM were proposed and, here, those variants are tested in order to asses their performance when solving CMOPs. The results obtained in the computational experiments are used to compare the proposal with another well known constraint handling scheme in the literature.

关键词： constrained multiobjective optimization adaptive penalty method differential evolution

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Enhancing Algorithm Performance Prediction in constrained multiobjective optimization Using Additional Training Problems

Enhancing Algorithm Performance Prediction in Constrained Mu...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Andova, Andrejaana Cork, Jordan N. Tusar, Tea Filipic, Bogdan Jozef Stefan Inst Jozef Stefan Int Postgrad Sch Ljubljana Slovenia

ISBN: (纸本)9798400704949

A research problem studied extensively in recent years is the prediction of optimization algorithm performance. A common approach is using the landscape features of optimization problems to train machine learning models. These models are then used to predict algorithm performance. Due to the small number of constrained multiobjective optimization problems (CMOPs) available for bench-marking, training a machine learning model to predict algorithm performance is a hard task. To address this issue, this study uses the functions from the bbob and bbob-constrained benchmark problems to generate new CMOPs. These are then used as additional training examples for the machine learning models. Given the large number of generated CMOPs, the experiments in this study are limited to those with two objectives and two variables. The obtained results are promising. Using additional problems in the training phase improves the predictions in half of the defined classification tasks.

关键词： constrained multiobjective optimization exploratory landscape analysis algorithm performance prediction

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Dynamic Landscape Analysis for constrained multiobjective optimization Problems 36th

Dynamic Landscape Analysis for Constrained Multiobjective Op...

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36th Australasian Joint Conference on Artificial Intelligence (AI)

作者： Alsouly, Hanan Kirley, Michael Munoz, Mario Andres Univ Melbourne Sch Comp & Informat Syst Melbourne Vic Australia ARC Ctr Optimisat Technol Integrated Methodol & A Melbourne Vic Australia Imam Mohammad Ibn Saud Islamic Univ Coll Comp & Informat Sci Riyadh Saudi Arabia

ISBN: (纸本)9789819983872;9789819983889

Landscape analysis is a data-driven approach that involves sampling the search space of an optimization problem to generate a range of statistical features. These features serve to characterize the 'problem difficulty'. However, the computational costs associated with offline independent sampling can be excessive, and this approach often overlooks valuable information accumulated by the optimization algorithm. This paper aims to expand our understanding of landscape analysis in the domain of black-box constrained multiobjective optimization problems. We demonstrate the potential of leveraging optimization algorithm trajectories to measure landscape features. Our findings underscore the significance of utilizing landscape features as a means to approximate algorithm performance, particularly in cases involving new instances lacking a known reference set. Ultimately, our goal is to employ landscape analysis to dynamically adapt algorithm constraint handling techniques.

关键词： constrained multiobjective optimization Adaptive landscape analysis Instance space analysis Evolutionary algorithms Constraint handling technique

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Evolutionary Algorithm with Dynamic Population Size for constrained multiobjective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 73卷

作者： Wang, Bing-Chuan Shui, Zhong-Yi Feng, Yun Ma, Zhongwei Cent South Univ Sch Automation Changsha 410083 Peoples R China Hunan Xiangjiang Artificial Intelligence Acad Changsha Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Hunan Univ Natl Res Ctr Robot Visual Percept & Control Techno Changsha 410082 Peoples R China

The core task of constrained multiobjective optimization is to achieve a tradeoff between exploration and exploitation as well as a tradeoff between constraints and objectives. We present an effective evolutionary algorithm with a dynamic population size (DPSEA) to achieve these two tradeoffs. In order to balance exploration and exploitation, the population size of DPSEA decreases continually as generation increases. In this manner, a bigger population size can encourage exploration in the early stage, while a smaller one can promote exploitation in the later stage. Aiming to balance constraints and objectives, a two-stage environmental selection strategy is proposed. In this strategy, objective information and constraint information is used to select two sets of solutions, respectively. Note that the sizes of these two sets are also adjusted dynamically. In this way, the information of both objectives and constraints can be used. Moreover, a novel mating selection strategy is designed to select promising parents. By assembling the above processes, DPSEA is able to achieve the two tradeoffs which are critical to constrained multiobjective optimization. Experiments on three sets of benchmark test functions owning difficult characteristics validate that DPSEA is competitive against some state-of-the-art constrained multiobjective optimization evolutionary algorithms.

关键词： constrained multiobjective optimization evolutionary algorithm dynamic population size two tradeoffs mating selection

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Adaptive design of the magnetron injection gun using constrained multiobjective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 83卷

作者： Wang, Pengbo Yang, Fan Li, Liang Chongqing Univ Sch Elect Engn Chongqing Peoples R China Huazhong Univ Sci & Technol Wuhan Natl High Magnet Field Ctr Wuhan Peoples R China

The design of the magnetron injection gun (MIG) is an inverse problem with implicit objective functions and complex constraints. The computer-aided design using an optimization algorithm is a promising method to improve the performance of the MIG and reduce design complexity and time consumption. However, the dependence on the initial structure makes it difficult to realize the real sense of using algorithms to design. To address this issue, this paper proposes an adaptive design method for the MIG. The design problem is equivalent to a constrained multiobjective optimization problem. A pseudo-random structure generation algorithm is developed based on the geometric constraints and the first-order MIG design trade-off equations. The fully parameterized MIG structure is randomly generated by the decision variables composed of the scale coefficients ranging from (0,1) and the other structural parameters. This transformation can increase the effectiveness of the randomly generated MIG structures and make it easier to set the value range of decision variables. The proposed adaptive design method is further verified by successfully designing the MIG for an 800 GHz gyrotron via the co-simulation of MATLAB and CST..

关键词： Magnetron injection gun Co-simulation method constrained multiobjective optimization Pseudo-random process Structure generation

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Purpose-directed two-phase multiobjective differential evolution for constrained multiobjective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 60卷

作者： Yu, Kunjie Liang, Jing Qu, Boyang Yue, Caitong Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China

When solving constrained multiobjective optimization problems by evolutionary algorithm, the key challenge is how to achieve the balance among convergence, diversity, and feasibility. To deal with this challenge, a purposedirected two-phase multiobjective differential evolution (PDTP-MDE) algorithm is developed in this paper. The main idea of PDTP-MDE is that the whole evolution process is divided into two sequential phases according to the expected purpose of each stage. To be specific, the first phase aims at keeping the balance between convergence and diversity, while the feasibility is taken as an auxiliary indicator. In this way, the population is capable of exploring different potential areas and avoiding to be trapped into local ones, thus providing more information about convergence and diversity for the later evolution process. Afterwards, the second phase mainly tends to maintain feasibility and diversity by selecting and using some promising infeasible solutions according to the population evolution status. In addition, an archive is maintained after each phase to preserve the superior feasible Pareto solutions found so far. By the above processes, the feasible Pareto front with well convergence and well diversity is obtained. The comprehensive experiments on 42 benchmark problems from three test suites demonstrate the superiority and competitiveness of the proposed PDTP-MDE, in comparison with other state-ofthe-art constrained multiobjective evolutionary algorithms.

关键词： constrained multiobjective optimization Evolutionary algorithm Constraint handling Purpose-directed strategy

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Design and analysis of helper-problem-assisted evolutionary algorithm for constrained multiobjective optimization

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INFORMATION SCIENCES 2023年第1期648卷

作者： Zhang, Yajie Tian, Ye Jiang, Hao Zhang, Xingyi Jin, Yaochu Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei Peoples R China Anhui Univ Sch Artificial Intelligence Hefei Peoples R China Bielefeld Univ Fac Technol Bielefeld Germany

In recent years, solving constrained multiobjective optimization problems (CMOPs) by introducing simple helper problems has become a popular concept. To date, no systematic study has investigated the conditions under which this concept operates. In this study, we presented a holistic overview of existing constrained multiobjective evolutionary algorithms (CMOEAs) to address three research questions: (1) Why do we introduce helper problems? (2) Which problems should be selected as helper problems? and (3) How do helper problems help? Based on these discussions, we developed a novel helper-problem-assisted CMOEA, where the original CMOP was solved by addressing a series of constraint-centric problems derived from the original problem, with their constraint boundaries shrinking gradually. At each stage, we also had an objectivecentric problem that was used to help solve the constraint-centric problem. In the experiments, we investigated the performance of the proposed algorithm on 66 benchmark problems and 15 real-world applications. The experimental results showed that the proposed algorithm is highly competitive compared with eight state-of-the-art CMOEAs.

关键词： Evolutionary algorithm constrained multiobjective optimization Inequality constraint Equality constraint

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Dual population approximate constrained Pareto front for constrained multiobjective optimization

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INFORMATION SCIENCES 2023年第1期648卷

作者： Zhou, Jinlong Zhang, Yinggui Suganthan, P. N. Cent South Univ Sch Traff & Transport Engn Changsha 410075 Peoples R China Qatar Univ Coll Engn KINDI Ctr Comp Res Doha Qatar

For constrained multiobjective optimization problems (CMOPs), the ultimate goal is to obtain a set of well-converged and well-distributed feasible solutions to approximate the constrained Pareto front (CPF). Various constraints may change the position and/or shape of the CPF. This poses great challenges to the approximation of the CPF. This is especially true when the CPF mainly lies on constraint boundaries (i.e., CPF and unconstrained PF have little or even no intersection). To tackle this issue, we propose a novel dual population algorithm for approximating the CPF from both sides of the constraint boundaries. Specifically, Population 1 uses the constrained-domination principle to approximate the CPF from the sides of feasible regions only;Population 2 adopts an improved c-constrained method to approximate the CPF from both the feasible as well as infeasible regions. Offspring generated by both populations are merged and combined with Population1 and Population2. In addition, some selected members of Population1 and Population 2 are permitted to migrate to the combined populations to facilitate knowledge sharing. Systematic experiments carried out on three benchmark test suites and 10 real-world CMOPs show the proposed algorithm achieved superior or competitive performance, especially for CMOPs where the CPF is mainly located at constraint boundaries. Therefore, on the basis of dual population, approximating CPFs from both sides of feasible and infeasible regions contributes an alternative approach to solving CMOPs.

关键词： constrained multiobjective optimization Dual population Constraint boundaries Evolutionary algorithms

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Interactive niching-based two-stage evolutionary algorithm for constrained multiobjective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 83卷

作者： Liang, Jing Zhang, Leiyu Yu, Kunjie Qu, Boyang Shang, Fuxing Qiao, Kangjia Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Zhengzhou 450007 Peoples R China Zhengzhou Univ Aeronaut Sch Humanity & Law Zhengzhou 450046 Peoples R China

When solving constrained multiobjective optimization problems (CMOPs), how to maintain diversity without losing convergence is a major challenge, because some small discrete feasible regions make the population hard to find a complete feasible Pareto Front. To this end, an interactive niching-based two-stage evolutionary algorithm for constrained multiobjective optimization, named INCMO, is proposed in this study. At each stage of INCMO, both two populations are employed to focus on different evolution purposes. Specifically, the main population optimizes the original CMOP, while the auxiliary population only considers the objectives by ignoring all constraints. INCMO uses two different niching mechanisms in two stages. In the early stage, each population independently uses the niching technique to generate offspring, to maintain diversity. In addition, two offspring populations are shared to realize migration between two populations. In the later stage, two populations cooperate to generate offspring based on an interactive niching technique. To be specific, two populations are merged and then divided into different niches. In this case, individuals from two populations will be assigned to the same niche. After that, individuals in the niche produce offspring through crossover and mutation. The process of generating offspring also promotes more effective migration between the two populations on the basis of increasing the diversity of population. Compared with other well-established methods, the proposed method in this study shows better performance on four test suites including 47 benchmark problems.

关键词： constrained multiobjective optimization constrained handling Niching mechanism Migration

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