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Computers and Operations Research - Special issue: Applications of integer programming
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Computers and Industrial Engineering
`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
Ant algorithms for discrete optimization
Artificial Life
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
Journal of Global Optimization
Cooperative Parallel Variable Neighborhood Search for the p-Median
Journal of Heuristics
DE/EDA: a new evolutionary algorithm for global optimization
Information Sciences—Informatics and Computer Science: An International Journal
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
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Applied Soft Computing
A hybrid genetic algorithm and particle swarm optimization for multimodal functions
Applied Soft Computing
Guest Editorial: Hybrid Metaheuristics
Computers and Operations Research
Evolutionary swarm cooperative optimization in dynamic environments
Natural Computing: an international journal
Two hybrid differential evolution algorithms for engineering design optimization
Applied Soft Computing
A unified view on hybrid metaheuristics
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
Using memory and fuzzy rules in a co-operative multi-thread strategy for optimization
Information Sciences: an International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
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This paper presents three hybrid metaheuristic algorithms that further improve the two hybrid differential evolution (DE) metaheuristic algorithms described in Liao [1]. The three improved algorithms are: (i) MDE'-HJ, which is a modification of MA-MDE' in Liao [1] by replacing the random walk with direction exploitation local search with the Hooke and Jeeves (HJ) method; (ii) MDE'-IHS-HJ, which is constructed by adding the Hooke and Jeeves method to the original cooperative hybrid, i.e., MDE'-IHS; and (iii) PSO-MDE'-HJ, which is a variation of MDE'-IHS-HJ by replacing improved harmony search (IHS) with particle search optimization (PSO). A comprehensive comparative study was carried out to compare the three improved hybrids with the three algorithms presented by Liao [1] in terms of average success rate, average function evaluations taken, average elapsed CPU time, and convergence profiles. A total of 18 problems, 4 more than those used in Liao [1], were selected from different engineering domains for testing. The test results indicate that all three new hybrids can achieve higher success rate in much less CPU time. Among these three hybrids, MDE'-IHS-HJ is the best one in terms of success rate, better than the best hybrid in Liao [1] by over 15% and better than the second best, PSO-MDE'-HJ, by nearly 10%.