Grasshopper optimization algorithm for multi-objective optimization problems

This work proposes a new multi-objective algorithm inspired from the navigation of grass hopper swarms in nature. A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone. A mechanism is then proposed to...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Vol. 48; no. 4; pp. 805 - 820
Main Authors: Mirjalili, Seyedeh Zahra, Mirjalili, Seyedali, Saremi, Shahrzad, Faris, Hossam, Aljarah, Ibrahim
Format: Journal Article
Language:English
Published: New York Springer US 01.04.2018
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Evolutionary algorithms
Machines
Manufacturing
Mechanical Engineering
Multiple objective analysis
Optimization
Pareto optimum
Processes
Optimization
ISSN:0924-669X, 1573-7497
Online Access:Get full text
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Summary:This work proposes a new multi-objective algorithm inspired from the navigation of grass hopper swarms in nature. A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone. A mechanism is then proposed to use the model in approximating the global optimum in a single-objective search space. Afterwards, an archive and target selection technique are integrated to the algorithm to estimate the Pareto optimal front for multi-objective problems. To benchmark the performance of the algorithm proposed, a set of diverse standard multi-objective test problems is utilized. The results are compared with the most well-regarded and recent algorithms in the literature of evolutionary multi-objective optimization using three performance indicators quantitatively and graphs qualitatively. The results show that the proposed algorithm is able to provide very competitive results in terms of accuracy of obtained Pareto optimal solutions and their distribution.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-017-1019-8