Bulletin of Electrical Engineering and Informatics Vol. 13, No. 1, February 2024, pp. 559∼571 ISSN: 2302-9285, DOI: 10.11591/eei.v13i1.6590 ❒ 559 Self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy Pirapong Singsathid, Jeerayut Wetweerapong, Pikul Puphasuk Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand Article Info Article history: Received Apr 27, 2023 Revised Jul 11, 2023 Accepted Aug 30, 2023 Keywords: Continuous optimization Differential evolution Mutation strategy Pheromone strategy Self-adaptation ABSTRACT Differential evolution (DE) is a population-based optimization algorithm widely used to solve a variety of continuous optimization problems. The self-adaptive DE algorithm improves the DE by encoding individual parameters to produce and propagate better solutions. This paper proposes a self-adaptive differen- tial evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy (SDE-FMP). The algorithm introduces the dynamical mutation opera- tion using the fitness rank of the individuals to divide the population into three groups and then select groups and their vectors with adaptive probabilities to create a mutant vector. Mutation and crossover operations use the encoded scal- ing factor and the crossover rate values in a target vector to generate the cor- responding trial vector. The values are changed according to the pheromone when the trial vector is inferior in the selection, whereas the pheromone is in- creased when the trial vector is superior. In addition, the algorithm also employs the resetting operation to unlearn and relearn the dominant pheromone values in the progressing search. The proposed SDE-FMP algorithm using the suitable resetting periods is compared with the well-known adaptive DE algorithms on several test problems. The results show that SDE-FMP can give high-precision solutions and outperforms the compared methods. This is an open access article under the CC BY-SA license. Corresponding Author: Pikul Puphasuk Deparment of Mathematics, Faculty of Science, Khon Kaen University Khon Kaen, 40002, Thailand Email: ppikul@kku.ac.th 1. INTRODUCTION Solving multimodal, high-dimensional, and non-linear real-world optimization problems requires well-designed efficiency optimization methods [1]–[5]. To address this challenge, many researchers have pro- posed evolutionary algorithms such as genetic algorithm (GA) [6], ant colony optimization (ACO) [7], particle swarm optimization (PSO) [8], [9], artificial bee colony algorithm (ABC) [10], and differential evolution (DE) [11], [12] for these problems. DE is a population-based global search algorithm introduced by Storn and Price in 1997 for continuous optimization. Its operations consist of mutation, crossover, and selection [11]. The performance of DE depends on the control parameters: scaling factor F and crossover rate CR. The scaling factor controls the step size of the mutation operation, and the crossover rate indicates the probability of exchanging elements between the mutant and target vectors. These control parameter values significantly affect the algorithm’s performance, and DE with the fixed parameters F and CR are only suitable for specific problems. Thus, many control parameter Journal homepage: http://beei.org