Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Particle swarm guided evolution strategy
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Advances in Differential Evolution
Advances in Differential Evolution
Computers and Operations Research
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Genetic and Evolutionary Computation Conference
A multi-objective particle swarm optimizer based on decomposition
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A hybrid evolutionary metaheuristics (HEMH) applied on 0/1 multiobjective knapsack problems
Proceedings of the 13th annual conference on Genetic and 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
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Hybrid evolutionary algorithms have been successfully applied to solve numerous multiobjective optimization problems (MOP). In this paper, a new hybrid evolutionary approach based on search strategy adaptation (HESSA) is presented. In HESSA, the search process is carried out through adopting a pool of different search strategies, each of which has a specified success ratio. A new offspring is generated using a randomly selected strategy. Then, according to the success of the generated offspring to update the population or the archive, the success ratio of the selected strategy is adapted. This provides the ability for HESSA to adopt the appropriate search strategy according to the problem on hand. Furthermore, the cooperation among different strategies leads to improve the exploration and the exploitation of the search space. The proposed pool is combined to a suitable evolutionary framework for supporting the integration and cooperation. Moreover, the efficient solutions explored over the search are collected in an external repository to be used as global guides. The proposed HESSA is verified against some of the state of the art MOEAs using a set of test problems commonly used in the literature. The experimental results indicate that HESSA is highly competitive and can be considered as a viable alternative.