Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective programming using uniform design and genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multi-Pareto-Ranking evolutionary algorithm
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Adaptive multi-objective genetic algorithm using multi-pareto-ranking
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Optimum oil production planning using infeasibility driven evolutionary algorithm
Evolutionary Computation
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By dividing the multiobjective optimization of the decision space into several small regions, this paper proposes multi-objective optimization algorithm based on subregional search, which makes individuals in same region operate each other by evolutionary operator and the information between the individuals of different regions exchange through their offsprings re-divided into regions again. Since the proposed algorithm utilizes the sub-regional search, the computational complexity at each generation is lower than the NSGA-II and MSEA. The proposed algorithm makes use of the max-min strategy with determined weight as fitness functions, which make it approach evenly distributed solution in Pareto front. This paper presents a kind of easy technology dealing with the constraint, which makes the proposed algorithm solved unconstrained multiobjective problems can also be used to solve constrained multiobjective problems. The numerical results, with 13 unconstrained multiobjective optimization testing instances and 10 constrained multiobjective optimization testing instances, are shown in this paper.