A constrained multi-objective optimization algorithm with two cooperative populations

IF 3.3 2区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Memetic Computing Pub Date : 2022-02-08 DOI:10.1007/s12293-022-00360-1

Jianlin Zhang, Jie Cao, Fuqing Zhao, Zuohan Chen

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引用次数: 3

Abstract

Constrained multi-objective problems (CMOPs) require balancing convergence, diversity, and feasibility of solutions. Unfortunately, the existing constrained multi-objective optimization algorithms (CMOEAs) exhibit poor performance when solving the CMOPs with complex feasible regions. To solve this shortcoming, this work proposes an improved algorithm named the CMOEA-TCP, which maintains two populations cooperating to push the solutions to approximate the constrained Pareto front. Specifically, one population is obtained by the Pareto-based method and aims to strengthen the algorithm’s convergence ability. Meanwhile, another population is maintained by decomposition-based method and devoted to improving its diversity. The two populations work cooperatively during the entire evolution process with the constraint-handling technique. The performance of the CMOEA- TCP is verified on three benchmark suites with 34 problems. The experimental results demonstrate that the CMOEA-TCP can achieve performance comparable to or better than the other six state-of-the-art CMOEAs on the majority of considered problems.

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两个合作种群的约束多目标优化算法

约束多目标问题(cmps)需要平衡解决方案的收敛性、多样性和可行性。然而，现有的约束多目标优化算法在求解具有复杂可行域的约束多目标优化问题时表现出较差的性能。为了解决这一问题，本文提出了一种改进的CMOEA-TCP算法，该算法保持两个种群的合作，将解推向近似约束Pareto前沿。具体来说，通过基于pareto的方法得到一个种群，旨在增强算法的收敛能力。同时，采用基于分解的方法维持另一个种群，并致力于提高其多样性。在整个进化过程中，两个种群通过约束处理技术协同工作。CMOEA- TCP在3个基准测试套件上测试了34个问题的性能。实验结果表明，在大多数考虑的问题上，CMOEA-TCP可以达到与其他六种最先进的cmoea相当或更好的性能。

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来源期刊

Memetic Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-OPERATIONS RESEARCH & MANAGEMENT SCIENCE

CiteScore

6.80

自引率

12.80%

发文量

期刊介绍： Memes have been defined as basic units of transferrable information that reside in the brain and are propagated across populations through the process of imitation. From an algorithmic point of view, memes have come to be regarded as building-blocks of prior knowledge, expressed in arbitrary computational representations (e.g., local search heuristics, fuzzy rules, neural models, etc.), that have been acquired through experience by a human or machine, and can be imitated (i.e., reused) across problems. The Memetic Computing journal welcomes papers incorporating the aforementioned socio-cultural notion of memes into artificial systems, with particular emphasis on enhancing the efficacy of computational and artificial intelligence techniques for search, optimization, and machine learning through explicit prior knowledge incorporation. The goal of the journal is to thus be an outlet for high quality theoretical and applied research on hybrid, knowledge-driven computational approaches that may be characterized under any of the following categories of memetics: Type 1: General-purpose algorithms integrated with human-crafted heuristics that capture some form of prior domain knowledge; e.g., traditional memetic algorithms hybridizing evolutionary global search with a problem-specific local search. Type 2: Algorithms with the ability to automatically select, adapt, and reuse the most appropriate heuristics from a diverse pool of available choices; e.g., learning a mapping between global search operators and multiple local search schemes, given an optimization problem at hand. Type 3: Algorithms that autonomously learn with experience, adaptively reusing data and/or machine learning models drawn from related problems as prior knowledge in new target tasks of interest; examples include, but are not limited to, transfer learning and optimization, multi-task learning and optimization, or any other multi-X evolutionary learning and optimization methodologies.