Abstract
Slime mould algorithm (SMA) is a population-based metaheuristic algorithm inspired by the phenomenon of slime mould oscillation. The SMA is competitive compared to other algorithms but still suffers from the disadvantages of unbalanced exploitation and exploration and is easy to fall into local optima. To address these shortcomings, an improved variant of SMA named MSMA is proposed in this paper. Firstly, a chaotic opposition-based learning strategy is used to enhance population diversity. Secondly, two adaptive parameter control strategies are proposed to balance exploitation and exploration. Finally, a spiral search strategy is used to help SMA get rid of local optimum. The superiority of MSMA is verified in 13 multidimensional test functions and 10 fixed-dimensional test functions. In addition, two engineering optimization problems are used to verify the potential of MSMA to solve real-world optimization problems. The simulation results show that the proposed MSMA outperforms other comparative algorithms in terms of convergence accuracy, convergence speed, and stability.
Highlights
Solving optimization problems means finding the best value of the given variables which satisfies the maximum or minimum objective value without violating the constraints
Metaheuristic optimization algorithms are able to obtain optimal or near-optimal solutions within a reasonable amount of time [3]. us they are widely used for solving optimization problems, such as mission planning [4,5,6,7], image segmentation [8,9,10], feature selection [11,12,13], and parameter optimization [14,15,16,17,18]
To further enhance the population diversity and overcome the deficiency that the reverse solution generated by the basic Opposition-based learning (OBL) is not necessarily better than the current solution, considering that chaotic mapping has the characteristics of randomness and ergodicity, it can help to generate new solutions and enhance the population diversity. erefore, this paper combines chaotic mapping with OBL and proposes a chaotic opposition-based learning strategy. e specific mathematical model is described as follows: XTi o lb + ub − λi · Xti, (8)
Summary
Solving optimization problems means finding the best value of the given variables which satisfies the maximum or minimum objective value without violating the constraints. Inspired by the phenomenon of slime oscillations, Li proposed a new population-based algorithm called slime mould algorithm (SMA) [36]. To further enhance the performance of SMA and considering that the NFL encourages us to continuously improve these existing algorithms, a modified variant of SMA called MSMA is proposed in this paper. A spiral search strategy is introduced to enhance the global exploration ability of the algorithm and avoid falling into local optimum.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
