Novel Hybrid GWO-WOA and BAT-PSO Algorithms for Solving Design Optimization Problems

This paper aims at the design and development of two hybrid nature inspired algorithms based on Grey Wolf Optimizer and Whale Optimization Algorithm (GWOWOA) and Binary Bat Optimization Algorithm and Particle Swarm Optimization Algorithm (BATPSO). Hybridization is a useful method to enhance the performance of these algorithms. The GWO algorithm is easy to fall into local optimum especially when it is used in the high-dimensional data. WOA algorithm experiences relatively low convergence precision and poor rate of convergence when it is applied in complex optimization problems. In this paper we embed bubble-net foraging method in WOA with that of prey encircling method in GWO. It has been observed experimentally that than just updating position vectors with respect to the three best fitness solutions, we were able to achieve faster convergence and better global optimum in most cases. In BATPSO, both algorithms are integrated and run in parallel and they perform a comparison between both minimum fitness function at each iteration. According to the observation, this greedy search algorithm in BAT optimization works best in higher values; however, finds it difficult in finding the global minimum as it reaches lower values; especially fractional fitness value. PSO is based on element-wise pos[i,j] search and updating the velocity to converge to a global minimum. The proposed hybrid algorithms are bench-marked using a set of 23 classical benchmark functions employed to test different characteristics of hybrid optimizers. The paper also performs solving two classical engineering design problems - Cantilever beam design and the multiple disc clutch brake problem. The results of the fitness functions prove that the proposed hybrid algorithms are able to produce better or very competitive output with respect to improved exploration, local optima avoidance, exploitation and convergence. All these hybrid algorithms find superior optimal designs for quintessential engineering problems engaged, showcasing that these algorithms are capable of solving constrained complex problems with diverse search spaces.