Abstract
The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function optimization. Fuzzy logic is applied to switch dynamically between evolutionary algorithms, in an attempt to improve the algorithm performance. The HEF hybrid evolutionary algorithms are compared to GA, PSO, GAPSO, and PSOGA. The comparison uses a variety of measurement functions. In addition to strongly convex functions, these functions can be uniformly distributed or not, and are valuable for evaluating our approach. Iterations of 500, 1000, and 1500 were used for each function. The HEF algorithm’s efficiency was tested on four functions. The new algorithm is often the best solution, HEF accounted for 75 % of all the tests. This method is superior to conventional methods in terms of efficiency
Highlights
Evolutionary computation helps solve difficult optimization problems
It is common to refer to genetic algorithms as evolutionary algorithms or evolutionary computation as, evolutionary strategies [1], learning classifier systems [2], evolutionary programming [3], differential evolution [4], genetic programming [5], and estimation of distribution algorithms as evolutionary algorithms or evolutionary computation [6]
Many papers [12, 13, 21, 22] mention Rosenbrock, Sphere, Rastrigin, and Griewank as benchmark functions for comparison in this study. These are common test functions that have been utilized in prior evolutionary optimization studies and provide a wide variety of challenges
Summary
The approach’s simplicity, robust response to changing circumstances, flexibility, and other features are all advantages but standard evolutionary algorithms have limited use in practical architectural design tasks. This may be due to the poor search efficiency and the lack of diversity of the result. Many ways emerged to overcome these weaknesses To determine this problem, look at the performance and quality of algorithms. It is common to refer to genetic algorithms as evolutionary algorithms or evolutionary computation as, evolutionary strategies [1], learning classifier systems [2], evolutionary programming [3], differential evolution [4], genetic programming [5], and estimation of distribution algorithms as evolutionary algorithms or evolutionary computation [6]. These algorithms share the same concep tual framework for simulating individual structure evolution but differ in issue description, selection method, and estimation of distribution algorithms
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