An Adaptive Sand Cat Swarm Algorithm Based on Cauchy Mutation and Optimal Neighborhood Disturbance Strategy

Abstract

Sand cat swarm optimization algorithm (SCSO) keeps a potent and straightforward meta-heuristic algorithm derived from the distant sense of hearing of sand cats, which shows excellent performance in some large-scale optimization problems. However, the SCSO still has several disadvantages, including sluggish convergence, lower convergence precision, and the tendency to be trapped in the topical optimum. To escape these demerits, an adaptive sand cat swarm optimization algorithm based on Cauchy mutation and optimal neighborhood disturbance strategy (COSCSO) are provided in this study. First and foremost, the introduction of a nonlinear adaptive parameter in favor of scaling up the global search helps to retrieve the global optimum from a colossal search space, preventing it from being caught in a topical optimum. Secondly, the Cauchy mutation operator perturbs the search step, accelerating the convergence speed and improving the search efficiency. Finally, the optimal neighborhood disturbance strategy diversifies the population, broadens the search space, and enhances exploitation. To reveal the performance of COSCSO, it was compared with alternative algorithms in the CEC2017 and CEC2020 competition suites. Furthermore, COSCSO is further deployed to solve six engineering optimization problems. The experimental results reveal that the COSCSO is strongly competitive and capable of being deployed to solve some practical problems.

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

“…Many metaheuristic algorithms have recently been reported in addition to the above-discussed algorithms for numerical and real-world engineering design optimization problems, including data clustering. For instance, ant colony optimization 35 , firefly algorithm 36 , 37 , flower pollination algorithm 38 , grey wolf optimizer (GWO) 39 – 42 , Jaya algorithm 43 , Teaching–learning based optimization (TLBO) algorithm 44 , Rao algorithm 45 , political optimizer 46 , whale optimization algorithm (WOA) 47 , Moth flame algorithm (MFO) 48 , multi-verse optimizer (MVO) 49 , Salp swarm algorithm (SSA) 50 , 51 , spotted hyena optimizer 52 , butterfly optimization 53 , lion optimization 54 , fireworks algorithm 55 , Cuckoo search algorithm 56 , bat algorithm 57 , Tabu search 58 , harmony search algorithm 59 , Newton–Raphson optimizer 60 , reptile search algorithm 61 , slime mould algorithm 62 , 63 , harris hawk optimizer 64 , Chimp optimizer 65 , artificial gorilla troop optimizer 66 , atom search algorithm 67 , marine predator algorithm 68 , 69 , sand cat swarm algorithm 70 , equilibrium optimizer 71 , 72 , Henry gas solubility algorithm (HGSA) 73 , resistance–capacitance algorithm 74 , arithmetic optimization algorithm 75 , quantum-based avian navigation optimizer 76 , multi trail vector DE algorithm 10 , 77 , arithmetic optimization algorithm 78 , starling murmuration optimizer 79 , atomic orbit search (AOS) 80 , subtraction-average-based optimizer 81 , etc. are reported for solving optimization problems.…”

Improved Multi-Strategy Sand Cat Swarm Optimization for Solving Global Optimization

“…Amir et al [43] proposed combining the sand cat swarm optimization algorithm with reinforcement learning techniques to improve its global optimization performance. Wang et al [44] proposed a chaos-based oppositional adaptive Cauchy sand cat swarm optimization algorithm. The algorithm balances exploration and exploitation through a nonlinear adaptive parameter and introduces a Cauchy variation operator to perturb the search step size.…”

An Improved Sand Cat Swarm Optimization with Lens Opposition-based Learning and Sparrow Search Algorithm

“…Finally, the lens opposition-based learning strategy is introduced to increase the convergence speed of the algorithm. Wang et al [22] proposed an adaptive sand cat swarm optimization algorithm based on Cauchy mutation and optimal neighborhood disturbance strategy (COSCSO). The algorithm introduces a nonlinear adaptive parameter balancing algorithm in the exploitation phase and the exploration phase, and uses the Cauchy mutation strategy and optimal neighborhood disturbance strategy to prevent it from falling into a local optimum, to improve the exploitation of the algorithm, and to increase the convergence speed of the algorithm.…”