Advances in Evolutionary Algorithms: Theory, Design and Practice (Studies in Computational Intelligence)
Multiobjective real-coded bayesian optimization algorithmrevisited: diversity preservation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A similarity-based mating scheme for evolutionary multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Adaptive elitist-population based genetic algorithm for multimodal function optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A diversity preserving selection in multiobjective evolutionary algorithms
Applied Intelligence
A population adaptive based immune algorithm for solving multi-objective optimization problems
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
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 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
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation
IEEE Transactions on Evolutionary Computation
Using unconstrained elite archives for multiobjective optimization
IEEE Transactions on Evolutionary Computation
Rank-density-based multiobjective genetic algorithm and benchmark test function study
IEEE Transactions on Evolutionary Computation
An Investigation on Preference Order Ranking Scheme for Multiobjective Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
IEEE Transactions on Evolutionary Computation
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
IEEE Transactions on Evolutionary Computation
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This paper presents a hybrid multiobjective evolutionary algorithm (HMEA) that efficiently deals with multiobjective optimization problems (MOPs). The aim is to discover new nondominated solutions in the neighborhood of the most promising individuals in order to effectively push individuals toward the global Pareto front. It can be achieved by bringing the strength of an adaptive local search (ALS) to bear upon the evolutionary multiobjective optimization. The ALS is devised by combining a weighted fitness strategy and a knowledge-based local search which does not incur any significant computational cost. To be more exact, the highly converged and less crowded solutions selected in accordance with the weighted fitness values are improved by the local search, thereby helping multiobjective evolutionary algorithms (MEAs) to economize on the search time and traverse the search space. Thus, the proposed HMEA that transplants the ALS to the framework of MEAs can achieve higher proximity and better diversity of nondominated solutions. To show the utility of HMEA, the ALS for multiobjective knapsack problems (MKPs) is developed by exploiting the problem's knowledge. Experimental results on the MKPs have provided evidence for its effectiveness as regards the proximity and the diversity performances.