An Enhanced Secretary Bird Optimization Algorithm Based on Multi Population Management for Numerical Optimization Problems
Jin Zhu, Bojun Liu, Jun Zheng, Shaojie Yin, Meng Wang
TL;DR
This paper improves the Secretary Bird Optimization Algorithm to better solve numerical optimization problems by balancing exploration and exploitation.
Contribution
The novel MESBOA algorithm integrates multi-population management and experience-trend guidance to enhance optimization performance.
Findings
MESBOA achieves faster convergence and higher accuracy on CEC2017 and CEC2022 test suites.
The algorithm shows robustness with mean rankings of 1.828 to 2.500 across different dimensions.
MESBOA successfully solves engineering constrained optimization problems in real-world scenarios.
Abstract
The Secretary Bird Optimization Algorithm (SBOA) is a novel swarm-based meta-heuristic that formulates an optimization model by mimicking the secretary bird’s hunting and predator-evasion behaviors, and thus possesses appreciable application potential. Nevertheless, it suffers from an unbalanced exploration–exploitation ratio, difficulty in maintaining population diversity, and a tendency to be trapped in local optima. To eliminate these drawbacks, this paper proposes an SBOA variant (MESBOA) that integrates a multi-population management strategy with an experience-trend guidance strategy. The proposed method is compared with eight advanced basic/enhanced algorithms of different categories on both the CEC2017 and CEC2022 test suites. Experimental results demonstrate that MESBOA delivers faster convergence, more stable robustness and higher accuracy, achieving mean rankings of 2.500…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · UAV Applications and Optimization · Advanced Multi-Objective Optimization Algorithms