# Multi-Strategy-Improvement-Based Slime Mould Algorithm
## 1 Introduction  
A considerable number of scientific and engineering problems can be cast as optimization problems, which can be effectively addressed by a range of metaheuristic algorithms. The stochastic nature of these algorithms helps circumvent local optima and yield satisfactory outcomes, leading to their extensive utilization in global optimization. In recent years, numerous novel metaheuristic algorithms have emerged, including the sailfish optimization algorithm [1], the tuna swarm optimization algorithm [2], and the artificial jellyfish search algorithm [3]. These algorithms have found widespread application in trajectory planning [4] and engineering optimization. The slime mold optimization (SMA) algorithm is a recently developed metaheuristic algorithm that was proposed by [PERSON] et al. in 2022 [5]. It is important to note that the term \"mould\" is used in British English while \"mold\" is used in American English. The paper [5] was written in British English, and to facilitate the retrieval of the article by researchers interested in SMA, the British English term \"mould\" has been retained in the title and keywords while the American English term \"mold\" is used in the subsequent sections. SMA is inspired by the oscillatory behavior and adaptive network formation mechanism exhibited by slime molds during the foraging process. By simulating the dynamic adjustment process of optimal paths by slime molds when exploring food sources, the algorithm exhibits strong global search capability and robustness, and has been successfully applied in the fields of image segmentation, engineering optimization, and neural network training. However, with the increasing complexity of optimization problems (e.g., high-dimensional, multimodal, dynamic environments, etc. ), SMA gradually exposes limitations in the convergence speed, local development capability, and constraint handling and urgently needsto be improved to enhance its performance. The local exploitation ability of SMA is insufficient, and in the late iteration, the contraction behavior of slime mold individuals may lead to decreases in population diversity and fall into the local optimum. In addition, the performance of SMA is degraded when dealing with high-dimensional problems, and the search efficiency of the algorithm decreases significantly with an increase in the dimensionality of the variables, which is manifested by the slow convergence speed. Therefore, many researchers have conducted many studies and explorations on SMA. In an effort to address these limitations, [PERSON] et al. have proposed a combination of the arithmetic optimization algorithm with the slime mold algorithm. They have done so by replacing the SMA contraction mechanism with the multiplication and division operator in the local development phase. This modification was made with the intention of enhancing the algorithm's stochasticity and ability to jump out of the local extremes [6]. [PERSON] et al. have applied elite backward learning and quadratic interpolation to the sticky bacteria algorithm. This modification was made with the intention of improving the algorithm's optimization accuracy, optimization speed, and robustness [7]. [PERSON] et al. designed a nonlinear adaptive inertia weight factor to improve the linear convergence method to nonlinear convergence and used the weight value to update the position of sticky bacteria to improve the convergence speed of the sticky bacteria algorithm [8]. Despite the extensive research and improvements on SMA by numerous experts, there remains significant room for development. To further enhance SMA's performance, this article proposes a multi-strategy-improvement-based slime mold algorithm (MSSMA). Firstly, chaotic mapping and inverse learning are used to generate a superior initial population, thereby enhancing the algorithm's convergence accuracy. Secondly, a balancing factor B is proposed, based on the ordering of fitness values. This is intended to help the algorithm balance between exploration and exploitation.