A partition-based convergence framework for population-based optimization algorithms

Li, Xinxin; Hua, Shuai; Liu, Qunfeng; Li, Yun

doi:10.1016/j.ins.2023.01.085

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…This is because MH algorithms generate a feasible solution space with a stochastic algorithm, search the space for solutions in each iteration, evaluate individual fitness through the fitness function, and perform updates to produce the optimal solution 3 . The MH algorithms have shown advantages in many fields, including global optimization 4 , 5 , feature selection 6 , 7 , sentiment classification 8 – 10 , and case forecasting 11 . Considering the no free lunch (NFL) theorem, no algorithm can perform well on every optimization problem 12 , it is significant to study the MH algorithms.…”

Section: Introductionmentioning

confidence: 99%

An improved manta ray foraging optimization algorithm

Qu,

Yuan,

et al. 2024

Sci Rep

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The Manta Ray Foraging Optimization Algorithm (MRFO) is a metaheuristic algorithm for solving real-world problems. However, MRFO suffers from slow convergence precision and is easily trapped in a local optimal. Hence, to overcome these deficiencies, this paper proposes an Improved MRFO algorithm (IMRFO) that employs Tent chaotic mapping, the bidirectional search strategy, and the Levy flight strategy. Among these strategies, Tent chaotic mapping distributes the manta ray more uniformly and improves the quality of the initial solution, while the bidirectional search strategy expands the search area. The Levy flight strategy strengthens the algorithm’s ability to escape from local optimal. To verify IMRFO’s performance, the algorithm is compared with 10 other algorithms on 23 benchmark functions, the CEC2017 and CEC2022 benchmark suites, and five engineering problems, with statistical analysis illustrating the superiority and significance of the difference between IMRFO and other algorithms. The results indicate that the IMRFO outperforms the competitor optimization algorithms.

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