Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. (December 2017)

• Record Type: Journal Article
• Authors: Mirjalili, Seyedali; Gandomi, Amir H.; Mirjalili, Seyedeh Zahra; Saremi, Shahrzad; Faris, Hossam; Mirjalili, Seyed Mohammad
• Is Part Of: Advances in engineering software. Volume 114(2017)
• Page Start: 163
• Page End: 191
• Publication Date: 2017-12
• Subjects: Particle swarm optimization -- Multi-objective optimization -- Genetic algorithm -- Heuristic algorithm -- Algorithm -- Benchmark; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2017.07.002
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A novel optimization algorithm called Salp Swarm Optimizer (SSA) is proposed. Multi-objective Salp Swarm Algorithm (MSSA) is proposed to solve multi-objective problems. Both algorithms are tested on several mathematical optimization functions. Two challenging engineering design problems are solved: airfoil design and marine propeller design. The qualitative and quantitative results prove the efficiency of SSA and MSSA. Abstract: This work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA) and Multi-objective Salp Swarm Algorithm (MSSA) for solving optimization problems with single and multiple objectives. The main inspiration of SSA and MSSA is the swarming behaviour of salps when navigating and foraging in oceans. These two algorithms are tested on several mathematical optimization functions to observe and confirm their effective behaviours in finding the optimal solutions for optimization problems. The results on the mathematical functions show that the SSA algorithm is able to improve the initial random solutions effectively and converge towards the optimum. The results of MSSA show that this algorithm can approximate Pareto optimal solutions with high convergence and coverage. The paper also considers solving several challenging and computationally expensive engineering design problems (e.g. airfoil design and marine propeller design) using SSA and MSSA. The results of the real case studies demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.