A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Editorial: Special Issue on "Nature Inspired Problem-Solving"
Information Sciences: an International Journal
Harmony K-means algorithm for document clustering
Data Mining and Knowledge Discovery
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
Self-adaptive harmony search algorithm for optimization
Expert Systems with Applications: An International Journal
A harmony search algorithm with ensemble of parameter sets
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Harmony Search Based Supervised Training of Artificial Neural Networks
ISMS '10 Proceedings of the 2010 International Conference on Intelligent Systems, Modelling and Simulation
Ensemble strategies with adaptive evolutionary programming
Information Sciences: an International Journal
A novel global harmony search algorithm for reliability problems
Computers and Industrial Engineering
Solving the sum-of-ratios problems by a harmony search algorithm
Journal of Computational and Applied Mathematics
Harmony Search Algorithms for Structural Design Optimization
Harmony Search Algorithms for Structural Design Optimization
Expert Systems with Applications: An International Journal
Harmony filter: A robust visual tracking system using the improved harmony search algorithm
Image and Vision Computing
Structural and Multidisciplinary Optimization
Expert Systems with Applications: An International Journal
Dynamic multi-swarm particle swarm optimizer with harmony search
Expert Systems with Applications: An International Journal
Review Article: Solving 0-1 knapsack problem by a novel global harmony search algorithm
Applied Soft Computing
Dynamic economic load dispatch using hybrid swarm intelligence based harmony search algorithm
Expert Systems with Applications: An International Journal
The variants of the harmony search algorithm: an overview
Artificial Intelligence Review
Cellular particle swarm optimization
Information Sciences: an International Journal
Self-adaptive learning based particle swarm optimization
Information Sciences: an International Journal
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Recent Advances in Harmony Search Algorithm
Recent Advances in Harmony Search Algorithm
Survey A survey on applications of the harmony search algorithm
Engineering Applications of Artificial Intelligence
Hybridising harmony search with a Markov blanket for gene selection problems
Information Sciences: an International Journal
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The Harmony Search (HS) algorithm is a population-based metaheuristic optimisation algorithm. This algorithm is inspired by the music improvisation process in which the musician searches for harmony and continues to polish the pitches to obtain a better harmony. Although several variants of the HS algorithm have been proposed, their effectiveness in dealing with diverse problems is still unsatisfactory. The performances of these variants mainly depend on the selection of different parameters of the algorithm. In this paper, a new variant of the HS algorithm is proposed that maintains a proper balance between diversification and intensification throughout the search process by automatically selecting the proper pitch adjustment strategy based on its Harmony Memory. However, the performance of the proposed Intelligent Tuned Harmony Search (ITHS) algorithm is influenced by other parameters, such as the Harmony Memory Size (HMS) and the Harmony Memory Considering Rate (HMCR). The effects that varying these parameters have on the performance of the ITHS algorithm is also analysed in detail. The performance of the proposed ITHS algorithm is investigated and compared with eight state-of-the-art HS variants over 17 benchmark functions. Furthermore, to investigate the robustness of the proposed algorithm at higher dimensions, a scalability study is also performed. Finally, the numerical results obtained reflect the superiority of the proposed ITHS algorithm in terms of accuracy, convergence speed, and robustness when compared with other state-of-the-art HS variants.