The Exploration of Wild Horse Optimization in Reliability Redundancy Allocation Problems

Abstract

In this work, the wild horse optimization (WHO) algorithm, known for its ease of use, efficiency, and fast convergence, is explored in solving the reliability redundancy allocation problem (RRAP) for series-parallel systems. This problem has as of late caught the attention of researchers in this area, especially in today’s rapidly growing field of artificial intelligence. The NP-hard RRAP problem deals with maximizing of reliability under certain constraints. This work uses WHO algorithm to maximize the overall system reliability by determining how many redundant components are to be used along with their reliabilities in each subsystem, such reliability is constrained by cost, volume, and weight. Testing is carried out to show the effectiveness of this algorithm using four known numerical examples, results are to be compared with simplified swarm algorithm (SSO), attraction-repulsion imperialist competitive algorithm (AR-ICA), hybrid salp swarm algorithm and teaching-learning based optimization (HSSTLBO), particle swarm optimization (PSO), and gradient based optimization (GBO). Computational results show that WHO was able to find better feasible near-optimal solutions effectively and efficiently in terms of population size and number of iterations.