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09-01-2021 | Methodologies and Application

Solving engineering optimization problems using an improved real-coded genetic algorithm (IRGA) with directional mutation and crossover

Authors: Amit Kumar Das, Dilip Kumar Pratihar

Published in: Soft Computing | Issue 7/2021

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Abstract

Genetic algorithm (GA) is used to solve a variety of optimization problems. Mutation operator also is responsible in GA for maintaining a desired level of diversity in the population. Here, a directional mutation operator is proposed for real-coded genetic algorithm (RGA) along with a directional crossover (DX) operator to improve its performance. These evolutionary operators use directional information to guide the search process in the most promising area of the variable space. The performance of an RGA with the proposed mutation operator and directional crossover (DX) is tested on six benchmark optimization problems of different complexities, and the results are compared to that of the RGAs with five other mutation schemes. The proposed IRGA is found to outperform other RGAs in terms of accuracy in the solutions, convergence rate, and computational time, which is established firmly through statistical analysis. Furthermore, the performance of the proposed IRGA is compared to that of a few recently proposed optimization algorithms. The proposed IRGA is seen to yield the superior results compared to that of the said techniques. It is also applied to solve five constrained engineering optimization problems, where again, it has proved its supremacy. The proposed mutation scheme using directional information leads to an efficient search, and consequently, a superior performance is obtained.

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Title: Solving engineering optimization problems using an improved real-coded genetic algorithm (IRGA) with directional mutation and crossover
Authors: Amit Kumar Das
Dilip Kumar Pratihar
Publication date: 09-01-2021
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 7/2021
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-020-05545-9

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