Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Ant colony optimization theory: a survey
Theoretical Computer Science
Journal of Global Optimization
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
A survey of particle swarm optimization applications in electric power systems
IEEE Transactions on Evolutionary Computation
Self-adaptive harmony search algorithm for optimization
Expert Systems with Applications: An International Journal
An Improved Harmony Search Algorithm with Differential Mutation Operator
Fundamenta Informaticae - Swarm Intelligence
Harmony Search Based Supervised Training of Artificial Neural Networks
ISMS '10 Proceedings of the 2010 International Conference on Intelligent Systems, Modelling and Simulation
The variants of the harmony search algorithm: an overview
Artificial Intelligence Review
A survey on parallel ant colony optimization
Applied Soft Computing
Expert Systems with Applications: An International Journal
Harmony search for generalized orienteering problem: best touring in China
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Two-layer particle swarm optimization with intelligent division of labor
Engineering Applications of Artificial Intelligence
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Melody Search (MS) Algorithm as an innovative improved version of Harmony Search optimization method, with a novel Alternative Improvisation Procedure (AIP) is presented in this paper. MS algorithm mimics performance processes of the group improvisation for finding the best succession of pitches within a melody. Utilizing different player memories and their interactive process, enhances the algorithm efficiency compared to the basic HS, while the possible range of variables can be varied going through the algorithm iterations. Moreover, applying the new improvisation scheme (AIP) makes algorithm more capable in optimizing shifted and rotated unimodal and multimodal problems than the basic MS. In order to demonstrate the performance of the proposed algorithm, it is successfully applied to various benchmark optimization problems. Numerical results reveal that the proposed algorithm is capable of finding better solutions when compared with well-known HS, IHS, GHS, SGHS, NGHS and basic MS algorithms. The strength of the new meta-heuristic algorithm is that the superiority of the algorithm over other compared methods increases when the dimensionality of the problem or the entire feasible range of the solution space increases.