Journal Article10.1016/J.ASOC.2017.06.053
An evolutionary algorithm with directed weights for constrained multi-objective optimization
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TL;DR: This paper proposes a novel constraint-handling technique based on directed weights to deal with CMOPs, which outperforms four compared algorithms in terms of finding a set of well-distributed non-domination solutions.
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About: This article is published in Applied Soft Computing. The article was published on 01 Nov 2017. The article focuses on the topics: Multi-objective optimization & Evolutionary algorithm.
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Citations
Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization
TL;DR: A parameter-free constraint handling technique, a two-archive evolutionary algorithm, for constrained multiobjective optimization that maintains two collaborative archives simultaneously and develops a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status.
Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons
Zhongwei Ma,Yong Wang +1 more
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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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
Joint Optimization of Energy Consumption and Latency in Mobile Edge Computing for Internet of Things
Laizhong Cui,Chong Xu,Shu Yang,Joshua Zhexue Huang,Jianqiang Li,Xizhao Wang,Zhong Ming,Nan Lu +7 more
TL;DR: This paper formalizes the problem into a constrained multiobjective optimization problem and finds the optimal solutions by a modified fast and elitist nondominated sorting genetic algorithm (NSGA-II) and proposes a novel problem-specific encoding scheme and genetic operators in the proposed modified NSGA- II.
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Stochastic ranking for constrained evolutionary optimization
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