An efficient hybrid swarm intelligence optimization algorithm for solving nonlinear systems and clustering problems

This article proposes a new hybrid swarm intelligence optimization algorithm called monarch butterfly optimization (MBO) algorithm with cuckoo search (CS) algorithm, named MBOCS, for optimization problems. MBO algorithm is known for its disability to discover feasible solutions over different runs b...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 27; H. 13; S. 8867 - 8895
Hauptverfasser: Tawhid, Mohamed A., Ibrahim, Abdelmonem M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2023
Schlagworte:
Application of Soft Computing
Artificial Intelligence
Computational Intelligence
Control
Engineering
Mathematical Logic and Foundations
Mechatronics
Robotics
Cuckoo search
Monarch butterfly optimization
Clustering problem
Hybrid meta-heuristic
Nonlinear system of equations
ISSN:1432-7643, 1433-7479
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Zusammenfassung:This article proposes a new hybrid swarm intelligence optimization algorithm called monarch butterfly optimization (MBO) algorithm with cuckoo search (CS) algorithm, named MBOCS, for optimization problems. MBO algorithm is known for its disability to discover feasible solutions over different runs because it may trap in the local minima. Also, CS is a recent powerful algorithm, while it may consume a large number of function evaluations to get the optimal solution, and this is one of the disadvantages of this algorithm. MBOCS can circumvent the disadvantages of MBO and CS algorithms. In this work, we integrate MBO with CS to improve the quality of solutions to solve various optimization problems, namely unconstraint benchmark functions, nonlinear systems and clustering problems. We solve fifteen of CEC’15 benchmark functions and compare our results with various algorithms such as group search algorithm, harmony search, particle swarm optimization and other hybrid algorithms in the literature. Moreover, we apply MBOCS on ten known nonlinear systems and eight real-world data from UCI. The results of MBOCS were compared with other known algorithms in the literature. The experimental results show that the proposed hybrid algorithm is a competitive and promising method for solving such optimization problems and outperforms other compared algorithms.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-022-07780-8