Enhanced crow search algorithm with multi-stage search integration for global optimization problems

Abstract

Crow search algorithm (CSA), as a new swarm intelligence algorithm that simulates the crows’ behaviors of hiding and tracking food in nature, performs well in solving many optimization problems. However, while handling complex and high-dimensional global optimization problems, CSA is apt to fall into evolutionary stagnation and has slow convergence speed, low accuracy, and weak robustness. This is mainly because it only utilizes a single search stage, where position updating relies on random following among individuals or arbitrary flight of individuals. To address these deficiencies, a CSA with multi-stage search integration (MSCSA) is presented. Chaos and multiple opposition-based learning techniques are first introduced to improve original population quality and ergodicity. The free foraging stage based on normal random distribution and Lévy flight is designed to conduct local search for enhancing the solution accuracy. And the following stage using mixed guiding individuals is presented to perform global search for expanding the search space through tracing each other among individuals. Finally, the large-scale migration stage based on the best individual and mixed guiding individuals concentrates on increasing the population diversity and helping the population jump out of local optima by moving the population to a promising area. All of these strategies form multi-level and multi-granularity balances between global exploration and local exploitation throughout the evolution. The proposed MSCSA is compared with a range of other algorithms, including original CSA, three outstanding variants of CSA, two classical meta-heuristics, and six state-of-the-art meta-heuristics covering different categories. The experiments are conducted based on the complex and high-dimensional benchmark functions CEC 2017 and CEC 2010, respectively. The experimental and statistical results demonstrate that MSCSA is competitive for tackling large-scale complicated problems, and is significantly superior to the competitors.

Apiary Organizational-Based Optimization Algorithm: A New Nature-Inspired Metaheuristic Algorithm

“…The difference is in nutrition, where the worker is fed bee bread, while the queen is fed royal food, where the workers feed her. queen emerges from the hexagonal eye after 16 days [3]. b) Queen Replacement: there are several reasons behind queen replacement.…”

“…MHAs can be applied to a wide range of problems in several cases such as finding the optimal solution when it is impractical or computationally infeasible. The key two elements of any metaheuristic algorithm are exploitation and exploration [1][2][3][4]. Exploration means investigating the search space on a global scale, whereas exploitation refers to concentrating on a local region using information on reasonable solutions found in a particular region.…”

“…This is due to International Journal of Intelligent Engineering and Systems, Vol.17, No. 3,2024 DOI: 10.22266/ijies2024.0630.61 their extraordinary capability to select ideal features and eliminate redundant and irrelevant characteristics [5]. Flexibility and easy implementation are the main reasons behind using MHAs in different situations.…”

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

“…CSA with a cosine function and incorporating the opposition-based learning concept [40], 2020 2 0.1 CSA with chaos and multiple opposition-based learning techniques [41], 2023 2 0.1…”

“…The comparison between the obtained fitness value and the standard deviation showed that the hybrid outperformed state-of-the-art methods. An enhanced CSA with chaos and multiple opposition-based learning techniques for global optimization problems was presented in [41]. The modification offered three levels of balancing global exploration and local exploitation.…”