A review of evolutionary algorithms in solving large scale benchmark optimisation problems. (7th January 2022)

• Record Type: Journal Article
• Authors: Mohapatra, Prabhujit; Roy, Santanu; Das, Kedar Nath; Dutta, Saykat; Raju, M. Sri Srinivasa
• Is Part Of: International journal of mathematics in operational research. Volume 21:Number 1(2022)
• Page Start: 104
• Page End: 126
• Publication Date: 2022-01-07
• Subjects: swarm intelligence -- large scale global optimisation -- evolutionary algorithms; Operations research -- Mathematical models -- Periodicals; Operations research -- Mathematics -- Periodicals; Decision making -- Mathematical models -- Periodicals; 658.4033
• Languages: English
• ISSNs: 1757-5850

Abstract

Optimisation problems containing huge total of decision variables are termed as large scale global optimisation problems which are often considered as abundant challenges to the area of optimisation. With presence of large number of decision variables, these problems also used to have the property of nonlinearity, discontinuity and multi-modality. Hence, the nature-inspired optimisation algorithms based on stochastic approaches are termed as great saviours than the deterministic approaches to handle these problems. However, the nature inspired optimisation algorithms also suffer from the jinx of dimensionality in the decision variable space. With increase of dimensions in the decision variable space, the complexity of the problem also increases exponentially. Hence, there is an immense need of proper guidance of choosing capable nature inspired algorithms to solve real-life large scale optimisation problems. In this paper, an attempt has been made to select the elite algorithm with proper justification. Hence, a number of works have been presented to analyse the results and to tackle the difficulty.