Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems

•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:...

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Bibliographic Details
Published in:Advances in engineering software (1992) Vol. 114; pp. 163 - 191
Main Authors: Mirjalili, Seyedali, Gandomi, Amir H., Mirjalili, Seyedeh Zahra, Saremi, Shahrzad, Faris, Hossam, Mirjalili, Seyed Mohammad
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.12.2017
Subjects:
Algorithm
Benchmark
Genetic algorithm
Heuristic algorithm
Multi-objective optimization
Particle swarm optimization
Benchmark
Multi-objective optimization
Genetic algorithm
Heuristic algorithm
Particle swarm optimization
Algorithm
ISSN:0965-9978
Online Access:Get full text
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Summary:•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. 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.
ISSN:0965-9978
DOI:10.1016/j.advengsoft.2017.07.002