Covariance Matrix Adaptation for Multi-objective Optimization
Evolutionary Computation
A self-adaptive embedded chaotic particle swarm optimization for parameters selection of Wv-SVM
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
The variants of the harmony search algorithm: an overview
Artificial Intelligence Review
A chaotic harmony search algorithm for the flow shop scheduling problem with limited buffers
Applied Soft Computing
Expert Systems with Applications: An International Journal
Design optimization with chaos embedded great deluge algorithm
Applied Soft Computing
Chaotic sequences to improve the performance of evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Chaotic ant swarm approach for data clustering
Applied Soft Computing
Chaotic populations in genetic algorithms
Applied Soft Computing
Expert Systems with Applications: An International Journal
On chaotic simulated annealing
IEEE Transactions on Neural Networks
Computers & Mathematics with Applications
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The application of chaotic sequences can be an interesting alternative to provide search diversity in an optimization procedure, named chaos optimization algorithm (COA). Since the chaotic motion is pseudo-randomness and chaotic sequences are sensitive to the initial conditions, the search ability of COA is usually effected by the starting values. Considering this weakness, parallel chaos optimization algorithm (PCOA) is studied in this paper. To obtain optimum solution accurately, harmony search algorithm (HSA) is integrated with PCOA to form a novel hybrid algorithm. Different chaotic maps are compared and the impacts of parallel parameter on the hybrid algorithm are discussed. Several simulation results are used to show the effective performance of the proposed hybrid algorithm.