Journal Article10.1109/TEVC.2019.2896967
Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons
Zhongwei Ma,Yong Wang +1 more
288
TL;DR: This paper proposes a new constraint construction method to facilitate the systematic design of test problems and designs a new test suite consisting of 14 instances, which covers diverse characteristics extracted from real-world CMOPs and can be divided into four types.
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Abstract: For solving constrained multiobjective optimization problems (CMOPs), many algorithms have been proposed in the evolutionary computation research community for the past two decades. Generally, the effectiveness of an algorithm for CMOPs is evaluated by artificial test problems. However, after a brief review of current artificial test problems, we have found that they are not well-designed and fail to reflect the characteristics of real-world applications (e.g., small feasibility ratio). Thus, in this paper, we first propose a new constraint construction method to facilitate the systematic design of test problems. Then, on the basis of this method, we design a new test suite consisting of 14 instances, which covers diverse characteristics extracted from real-world CMOPs and can be divided into four types. Considering that the comprehensive performance comparisons among the constraint-handling techniques (CHTs) remain scarce, we choose several representative CHTs and compare their performance on our test suite. The performance comparisons identify the strengths and weaknesses of different CHTs on different types of CMOPs and provide guidelines on how to select/design a CHT in a specific scenario.
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Citations
A Coevolutionary Framework for Constrained Multiobjective Optimization Problems
TL;DR: A coevolutionary framework for constrained multiobjective optimization, which solves a complex CMOP assisted by a simple helper problem and is compared to several state-of-the-art algorithms tailored for CMOPs.
459
Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization.
TL;DR: Zhang et al. as mentioned in this paper proposed a two-stage evolutionary algorithm, which adjusts the fitness evaluation strategies during the evolutionary process to adaptively balance objective optimization and constraint satisfaction for solving constrained multiobjective optimization problems.
227
A Constrained Multiobjective Evolutionary Algorithm With Detect-and-Escape Strategy
TL;DR: The proposed detect-and-escape strategy uses the feasible ratio and the change rate of overall constraint violation to detect stagnation, and adjusts the weight of the constraint violation for guiding the search to escape from stagnation states.
191
A Survey on Evolutionary Constrained Multiobjective Optimization
TL;DR: In this paper , a comprehensive survey of evolutionary constrained multiobjective optimization algorithms is presented, where a large number of CMOEAs through categorization and analysis of their advantages and drawbacks in each category are presented.
177
References
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An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
Kalyanmoy Deb,Himanshu Jain +1 more
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