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This paper introduces an enhanced version of the Capuchin Search Algorithm (CapSA) called ECapSA. CapSA draws inspiration from the collective intelligence of Capuchin monkeys and has shown success in solving real-world problems. However, it may encounter challenges handling complex optimization tasks, such as premature convergence or being trapped in local optima. ECapSA employs a local escaping mechanism operating the abandonment limit concept to exploit potential solutions and introduce diversification trends. Additionally, the ECapSA algorithm is improved by integrating the principles of the cooperative island model, resulting in the iECapSA. This modification enables better management of population diversity and a more optimal balance between exploration and exploitation. The efficiency of iECapSA is validated through a series of experiments, including the IEEE-CEC2014 benchmark functions and training the feedforward neural network (FNN) on seven biomedical datasets. The performance of iECapSA is compared to other metaheuristic techniques, namely differential evolution (DE), sine cosine algorithm (SCA), and whale optimization algorithm (WOA). The results of the comparative study demonstrate that iECapSA is a strong contender and surpasses other training algorithms in most datasets, particularly in terms of its ability to avoid local optima and its improved convergence speed.
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The Crow Search Algorithm (CSA) is a swarm-based metaheuristic algorithm that simulates the intelligent foraging behaviors of crows. While CSA effectively handles global optimization problems, it suffers from certain limitations, such as low search accuracy and a tendency to converge to local optima. To address these shortcomings, researchers have proposed modifications and enhancements to CSA’s search mechanism. One widely explored approach is the structured population mechanism, which maintains diversity during the search process to mitigate premature convergence. The island model, a common structured population method, divides the population into smaller independent sub-populations called islands, each running in parallel. Migration, the primary technique for promoting population diversity, facilitates the exchange of relevant and useful information between islands during iterations. This paper introduces an enhanced variant of CSA, called Enhanced CSA (ECSA), which incorporates the cooperative island model (iECSA) to improve its search capabilities and avoid premature convergence. The proposed iECSA incorporates two enhancements to CSA. Firstly, an adaptive tournament-based selection mechanism is employed to choose the guided solution. Secondly, the basic random movement in CSA is replaced with a modified operator to enhance exploration. The performance of iECSA is evaluated on 53 real-valued mathematical problems, including 23 classical benchmark functions and 30 IEEE-CEC2014 benchmark functions. A sensitivity analysis of key iECSA parameters is conducted to understand their impact on convergence and diversity. The efficacy of iECSA is validated by conducting an extensive evaluation against a comprehensive set of well-established and recently introduced meta-heuristic algorithms, encompassing a total of seventeen different algorithms. Significant differences among these comparative algorithms are established utilizing statistical tests like Wilcoxon’s rank-sum and Friedman’s tests. Experimental results demonstrate that iECSA outperforms the fundamental ECSA algorithm on 82.6% of standard test functions, providing more accurate and reliable outcomes compared to other CSA variants. Furthermore, Extensive experimentation consistently showcases that the iECSA outperforms its comparable algorithms across a diverse set of benchmark functions.
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