A Novel Parent Centric Crossover with the Log-Logistic Probabilistic Approach Using Multimodal Test Problems for Real-Coded Genetic Algorithms

Abstract

In this paper, a comprehensive empirical study is conducted to evaluate the performance of a new real-coded crossover operator called Fisk crossover (FX) operator. The basic aim of the proposed study is to preserve population diversity as well as to avoid local optima. In this context, a new crossover operator is designed and developed which is linked with Log-logistic probability distribution. For its global performance, a realistic comparison is made between FX versus double Pareto crossover (DPX), Laplace crossover (LX), and simulated binary crossover (SBX) operators. Moreover, these crossover operators are also used in conjunction with three mutation operators called power mutation (PM), Makinen, Periaux, and Toivanen mutation (MPTM), and nonuniform mutation (NUM) for inclusive evaluation. The performance of probabilistic-based algorithms is tested on a set of twenty-one well-known nonlinear optimization benchmark functions with diverse features. The empirical results show a substantial dominance of FX over other crossover operators with authentication of performance index (PI). Moreover, we also examined the significance of the proposed crossover scheme by administrating ANOVA and Gabriel pairwise multiple comparison test. Finally, the statistically significant results of the proposed crossover scheme have a definite edge over the other schemes, and it is also expected that FX has a great potential to solve complex optimization problems.

Installation of Smog-Free Towers Using Novel Real Coded Genetic Algorithm

“…In this crossover, only one offspring was produced and characterized as the convex linear combination of the parents, and gene positioning was chosen at random by exchanging the positions of their respective genes. Following that, various types of real coded crossover operators [29], [30], [31], [32], [33], [34] were developed to achieve faster convergence as well as greater accuracy and efficiency. We develop a novel real-coded crossover operator for this purpose.…”

An Improved Sparrow Search Algorithm for the Optimization of Variational Modal Decomposition Parameters

“…The crossover operator plays a crucial role in optimization algorithms by promoting population diversity and exploring the search space. The Laplace crossover operator (LX) is a particularly unique crossover operator that has been applied in various fields [25,26]. Deep et al [27] first introduced the LX in 2007 and applied it to genetic algorithms (GAs).…”