Journal Article10.1007/S10489-020-01969-W
Adaptively weighted decomposition based multi-objective evolutionary algorithm
Suraj S. Meghwani,Manoj Thakur +1 more
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TL;DR: This study proposes an adaptive strategy to modify these scalarizing weights after regular intervals by assessing the crowdedness of solutions using crowding distance operator and shows better performance when compared with other state-of-the-art multi-objective algorithms over most of the benchmark problems.
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Abstract: Multi-objective evolutionary algorithm based on Decomposition (MOEA/D) decomposes a multi-objective problem into a number of scalar optimization problems using uniformly distributed weight vectors. However, uniformly distributed weight vectors do not guarantee uniformity of solutions on approximated Pareto-Front. This study proposes an adaptive strategy to modify these scalarizing weights after regular intervals by assessing the crowdedness of solutions using crowding distance operator. Experiments carried out over several benchmark problems with complex Pareto-Fronts show that such a strategy helps in improving the convergence and diversity of solutions on approximated Pareto-Front. Proposed algorithm also shows better performance when compared with other state-of-the-art multi-objective algorithms over most of the benchmark problems.
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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
A Survey on Evolutionary Constrained Multiobjective Optimization
TL;DR: In this article , 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.
122
Multi-strategy ensemble firefly algorithm with equilibrium of convergence and diversity
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Solving the multi-objective job shop scheduling problems with overtime consideration by an enhanced NSGA-Ⅱ
Shuangyuan Shi,Hegen Xiong +1 more
TL;DR: This study proposes a multi-objective job shop scheduling problem with overtime consideration to minimize total tardiness and overtime costs, and develops an enhanced NSGA-II algorithm with a two-stage decoding scheme, adaptive mechanism, and local search procedure to optimize the problem.
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A coevolutionary constrained multi-objective algorithm with a learning constraint boundary
Jie Cao,Zesen Yan,Zuohan Chen,Jianlin Zhang +3 more
TL;DR: This paper proposes CCMOLCB, a coevolutionary constrained multi-objective algorithm that balances convergence, diversity, and feasibility by employing a learning constraint boundary and dynamic weight coefficient, outperforming seven state-of-the-art algorithms on five test suites.
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References
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Eckart Zitzler,Lothar Thiele +1 more
TL;DR: The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
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SPEA2: Improving the strength pareto evolutionary algorithm
Eckart Zitzler,Marco Laumanns,Lothar Thiele +2 more
- 01 Jan 2001
TL;DR: An improved version of SPEA, namely SPEA2, is proposed, which incorporates in contrast to its predecessor a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method.
6K
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
TL;DR: A reference-point-based many-objective evolutionary algorithm that emphasizes population members that are nondominated, yet close to a set of supplied reference points is suggested that is found to produce satisfactory results on all problems considered in this paper.