Search

Metaheuristic algorithms have garnered much attention among researchers owing to their robustness, adaptability, independence from a predetermined initial solution, and lack of reliance on gradient computations. The flower pollination algorithm (FPA) and the slime mould algorithm (SMA) are efficient methodologies for addressing global optimization challenges. Nonetheless, tackling large-scale global problems using a single algorithm often proves challenging due to inherent limitations in its mechanism. One effective approach to mitigating this limitation is to hybrid the two algorithms employing suitable strategies. We proposed a hybrid algorithm (GFPSMA) based on FPA and SMA. First, to address the global exploration issue of FPA, a method was proposed that utilized the golden section mechanism to enhance information exchange between random individuals and the best individual. Second, to improve the reliability of the random search phase in SMA, an adaptive step-size strategy was introduced. Furthermore, a dual-competition mechanism, inspired by gaming concepts, was introduced to enhance the integration of the two algorithms. Finally, an elite learning method with adjustment conditions was employed to refine the localization of the best individual. To assess the performance advantage of GFPSMA, 39 benchmark functions were employed, comparing GFPSMA with FPA and SMA along with their six variants, six variants of other metaheuristic algorithms, three CEC competition algorithms, totaling 17 algorithms, and strategic algorithms for testing. Experimental results demonstrated the favorable performance advantage of GFPSMA. Additionally, the feasibility and practicality of GFPSMA were demonstrated in four engineering problems. [ABSTRACT FROM AUTHOR]

In recent years, due to the growing complexity of real-world problems, researchers have been favoring stochastic search algorithms as their preferred method for problem solving. The slime mould algorithm is a high-performance, stochastic search algorithm inspired by the foraging behavior of slime moulds. However, it faces challenges such as low population diversity, high randomness, and susceptibility to falling into local optima. Therefore, this paper presents an enhanced slime mould algorithm that combines multiple strategies, called the ESMA. The incorporation of selective average position and Lévy flights with jumps in the global exploration phase improves the flexibility of the search approach. A dynamic lens learning approach is employed to adjust the position of the optimal slime mould individual, guiding the entire population to move towards the correct position within the given search space. In the updating method, an improved crisscross strategy is adopted to reorganize the slime mould individuals, which makes the search method of the slime mould population more refined. Finally, the performance of the ESMA is evaluated using 40 well-known benchmark functions, including those from CEC2017 and CEC2013 test suites. It is also recognized by Friedman's test as statistically significant. The analysis of the results on two real-world engineering problems demonstrates that the ESMA presents a substantial advantage in terms of search capability. [ABSTRACT FROM AUTHOR]