Grasp and Path Relinking for 2-Layer Straight Line Crossing Minimization
INFORMS Journal on Computing
A Hybrid GRASP with Perturbations for the Steiner Problem in Graphs
INFORMS Journal on Computing
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
A GRASP Algorithm for the Multi-Objective Knapsack Problem
QEST '04 Proceedings of the The Quantitative Evaluation of Systems, First International Conference
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Computers and Operations Research
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective optimization problems: test cases, approximations, and applications
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
HEMH2: an improved hybrid evolutionary metaheuristics for 0/1 multiobjective knapsack problems
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
A hybrid evolutionary approach with search strategy adaptation for mutiobjective optimization
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Handling Multiobjective Optimization Problems (MOOP) using Hybrid Metaheuristics represents a promising and interest area of research. In this paper, a Hybrid Evolutionary Metaheuristics (HEMH) is presented. It combines different metaheuristics integrated with each other to enhance the search capabilities. It improves both of intensification and diversification toward the preferred solutions and concentrates the search efforts to investigate the promising regions in the search space. In the proposed HEMH, the search process is divided into two phases. In the first one, the DM-GRASP is applied to obtain an initial set of high quality solutions dispersed along the Pareto front. Then, the search efforts are intensified on the promising regions around these solutions through the second phase. The greedy randomized path-relinking with local search or reproduction operators are applied to improve the quality and to guide the search to explore the non discovered regions in the search space. The two phases are combined with a suitable evolutionary framework supporting the integration and cooperation. Moreover, the efficient solutions explored over the search are collected in an external archive. The HEMH is verified and tested against some of the state of the art MOEAs using a set of MOKSP instances commonly used in the literature. The experimental results indicate that the HEMH is highly competitive and can be considered as a viable alternative.