Improved Remora Optimization Algorithm with Mutualistic Strategy for Solving Constrained Engineering Optimization Problems

Recently, a new swarm intelligence optimization algorithm called the remora (ROA) was proposed. ROA simulates remora’s behavior of adsorption host and uses some formulas sailfish (SFO) whale (WOA) to update solutions. However, performance is still unsatisfactory. When solving complex problems, ROA’s convergence ability requires further improvement. Moreover, it easy fall into local optimization. Since depends on obtain food optimize performance, this paper introduces mutualistic strategy strengthen symbiotic relationship between host. Meanwhile, chaotic tent mapping roulette wheel selection are added improve algorithm’s performance. By incorporating above improvements, proposes an improved with (IROA) 23 benchmark functions in different dimensions CEC2020 validate proposed IROA. Experimental studies six classical engineering problems demonstrate that IROA has excellent advantages practical problems.