A review of evolutionary algorithms in solving large scale benchmark optimisation problems

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.

Fast random opposition-based learning Aquila optimization algorithm

“…t-test: Two-tailed t -tests [12] has been used to compare different statistical outcomes at a consequence of 0.05. The t values are determined with the help of and Std values.…”

“…These methods range from traditional techniques that employ either linear or non-linear programming methods [10] to newly developed, nature-inspired methods, each with its own set of advantages and disadvantages. Traditional approaches, while effective in tackling well-known optimization issues [11] , [12] , [13] , have two drawbacks: they require a full-promise initial start vector within the search area, and they are inherently dependent on gradient information [14] , [15] . Also, traditional approaches can no longer be employed to address challenging real-world problems as an outcome of the improvement of technology and science in high society.…”

Learning cooking algorithm for solving global optimization problems

“…The modelling of biological sciences and genetics and the use of evolutionary operators like natural selection are the basis of evolutionary-based optimization algorithms 7 . One of the first evolutionary-based optimization algorithm, the genetic algorithm (GA) 8 , has been developed using selection, crossover, and mutation sequence operators and a model of the reproductive process.…”

Evolutionary Algorithm in the Evaluation Index System of Students' Comprehensive Moral Education Ability

“…Generally, the optimal solution of minimizing problem 1 will shift from the average weight of the questionnaire to the direction of the maximum weight density of the questionnaire as a whole. The weight of one indicator can be obtained for each solution of minimizing problem 1, and the weight of each indicator x1, x2,…,x K can be obtained by solving it for K times [4][5].…”