An improved multi-strategy beluga whale optimization for global optimization problems

Abstract

<abstract> <p>This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergence accuracy and speed of beluga whale optimization (BWO). In IBWO, we use a new group action strategy (GAS), which replaces the exploration phase in BWO. It was inspired by the group hunting behavior of beluga whales in nature. The GAS keeps individual belugas whales together, allowing them to hide together from the threat posed by their natural enemy, the tiger shark. It also enables the exchange of location information between individual belugas whales to enhance the balance between local and global lookups. On this basis, the dynamic pinhole imaging strategy (DPIS) and quadratic interpolation strategy (QIS) are added to improve the global optimization ability and search rate of IBWO and maintain diversity. In a comparison experiment, the performance of the optimization algorithm (IBWO) was tested by using CEC2017 and CEC2020 benchmark functions of different dimensions. Performance was analyzed by observing experimental data, convergence curves, and box graphs, and the results were tested using the Wilcoxon rank sum test. The results show that IBWO has good optimization performance and robustness. Finally, the applicability of IBWO to practical engineering problems is verified by five engineering problems.</p> </abstract>

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Hybrid Slime Mold and Arithmetic Optimization Algorithm with Random Center Learning and Restart Mutation

“…z the population position using Formula (6) vb, vc, and p. ate the population position using Formula (6) ate the value of parameter mop using Formula (9) < 0.5 date the population position using Formula (8) date the population position using Formula ( 8) If population position using SCLS population position using MS opulation position using RS rrent best solution st solution.…”

“…NFL’s law motivates researchers to enhance their ability to solve new problems by improving currently known algorithms. For example, Chen et al were inspired by the lifestyle of beluga whales and developed an IBWO [ 8 ] that improved the algorithm’s global optimization ability; Wen et al enhanced the global optimization capability of the algorithm by using a new host-switching mechanism [ 9 ]; Wu et al improved the sand cat’s wandering strategy and applied it to engineering problems [ 10 ].…”

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“…However, the beluga optimization algorithm still suffers from problems such as uneven population distribution, poor spatial search ability and prematurely falling into local optimality. Chen, Zhang, and Wang (2023) address the shortcomings of the standard beluga optimization algorithm, such as low convergence accuracy and limited adaptive ability and adopt Fuch chaotic initialization to improve the traversability of the algorithm's initialized population, thus enhancing the algorithm's optimization searching accuracy and introduce the Fuch chaotic mapping in the development stage to coordinate the algorithm's global searching and local searching, which effectively improves the algorithm's adaptive ability. Chen, Zheng, Li, Zhang, and Zhu (2023) propose an update elite group mechanism for the standard Beluga Whale algorithm which has the defects of being prone to falling into local optimum and losing suboptimal solutions; secondly, in order to enhance the exploratory ability of the algorithm, a reverse learning strategy is added at the same time.…”