Abstract
Optimization problems can be found in many aspects of our lives. An optimization problem can be approached as searching problem where an algorithm is proposed to search for the value of one or more variables that minimizes or maximizes an optimization function depending on an optimization goal. Multi-objective optimization problems are also abundant in many aspects of our lives with various applications in different fields in applied science. To solve such problems, evolutionary algorithms have been utilized including genetic algorithms that can achieve decent search space exploration. Things became even harder for multi-objective optimization problems when the algorithm attempts to optimize more than one objective function. In this paper, we propose a hybrid genetic algorithm (HGA) that utilizes a genetic algorithm (GA) to perform a global search supported by the particle swarm optimization algorithm (PSO) to perform a local search. The proposed HGA achieved the concept of rehabilitation of rejected individuals. The proposed HGA was supported by a modified selection mechanism based on the K-means clustering algorithm that succeeded to restrict the selection process to promising solutions only and assured a balanced distribution of both the selected to survive and selected for rehabilitation individuals. The proposed algorithm was tested against 4 benchmark multi-objective optimization functions where it succeeded to achieve maximum balance between search space exploration and search space exploitation. The algorithm also succeeded in improving the HGA’s overall performance by limiting the average number of iterations until convergence.
Highlights
1.1 Evolutionary-based algorithmsIn computer science, an evolutionary-based algorithm (EA) is an artificial intelligence technique that targets global optimization by mimicking the biological process of evolution
We propose a hybrid genetic algorithm (HGA) that utilizes a genetic algorithm (GA) to perform a global search supported by the particle swarm optimization algorithm (PSO) to perform a local search
GA is supposed to achieve the perfect balance between search space exploration and search space exploitation as assumed in Beasley et al (1993), such that ‘‘the population size is infinite and the fitness function accurately reflects the suitability of a solution and the gene interactions are minimum.’’ In practice, the population size is finite which affects both the performance and the sampling ability of the genetic algorithm
Summary
An evolutionary-based algorithm (EA) is an artificial intelligence technique that targets global optimization by mimicking the biological process of evolution. EAs operate by utilizing operators driven from biological evolution such as breeding, crossover, mutation, and selection (Li et al 2020; Nopiah et al 2010). EAs are population based where each individual in an EA’s population represents a possible solution to the optimization problem. The quality of a possible solution is determined by a fitness function that measures how good a candidate as a solution to the optimization problem. The evolution process in an EA commences by repeating the evolution operators mentioned above (Luo et al 2020)
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