access icon openaccess Multi-objective social spider optimisation algorithm

This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Received 18/07/2018, Accepted 26/07/2018, Published 16/08/2018

Considering that the social spider algorithm is still unable to solve the multi-objective optimisation problem, this study presents a multi-objective social spider optimisation algorithm. Firstly, a new normalised fitness value formula is proposed based on the multi-objective optimisation purposes, which is able to trade off the non-dominated rankings and crowded distances and evaluate individual strengths and weaknesses effectively; secondly, the gravitational factor is used in order to balance the impact of individual fitness and distance to individual performance, which improves the vibration perception ability of the calculation method as well; once again, the renewal pattern of the female and male population is improved in order to balance the convergence rate and population diversity of the algorithm; lastly, the environmental selection strategy which is based on cosine distance is proposed for female and male population renewal. Testing on the ZDT test set, experimental results show that, compared with the six representative multi-objective evolutionary algorithms, the proposed algorithm in this study has better distribution and better convergence performance.

Inspec keywords: evolutionary computation; optimisation

Other keywords: fitness value formula; non-dominated rankings; gravitational factor; ZDT test set; multi-objective evolutionary algorithms; multi-objective social spider optimisation algorithm

Subjects: Optimisation techniques; Optimisation; Optimisation techniques

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