Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems




evolutionary computations, GA, PSO, FLC, optimization, hybrid evolutionary algorithm


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

Author Biography

Maan Afathi, University of Mosul

Doctor of Computer Sciences, Teacher

Department of Computer Science

College of Education for Pure Sciences


Ishibuchi, H., Nojima, Y. (2007). Optimization of Scalarizing Functions Through Evolutionary Multiobjective Optimization. Evolutionary Multi-Criterion Optimization, 51–65. doi:

Butz, M. V. (2006). Rule-based evolutionary online learning systems. Springer-Verlag, 259. doi:

Coello, C. A. C., Lamont, G. B., Van Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems. Springer, 800. doi:

Deng, W., Shang, S., Cai, X., Zhao, H., Song, Y., Xu, J. (2021). An improved differential evolution algorithm and its application in optimization problem. Soft Computing, 25 (7), 5277–5298. doi:

Kuranga, C., Pillay, N. (2021). Genetic programming-based regression for temporal data. Genetic Programming and Evolvable Machines, 22 (3), 297–324. doi:

Lehre, P. K., Nguyen, P. T. H. (2021). Runtime Analyses of the Population-Based Univariate Estimation of Distribution Algorithms on LeadingOnes. Algorithmica, 83 (10), 3238–3280. doi:

Chen, P.-C., Chen, C.-W., Chiang, W.-L., Yeh, K. (2009). A novel stability condition and its application to ga-based fuzzy control for nonlinear systems with uncertainty. Journal of Marine Science and Technology, 17 (4). doi:

Chang, X., Lilly, J. H. (2004). Evolutionary Design of a Fuzzy Classifier From Data. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 34 (4), 1894–1906. doi:

Mohammadian, M., Stonier, R. J. (1994). Generating fuzzy rules by genetic algorithms. Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication. doi:

Chen, S.-M., Chen, Y.-C. (2002). Automatically constructing membership functions and generating fuzzy rules using genetic algorithms. Cybernetics and Systems, 33 (8), 841–862. doi:

Tsang, C.-H., Kwong, S., Wang, H. (2007). Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection. Pattern Recognition, 40 (9), 2373–2391. doi:

Angeline, P. J. (1998). Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences. Evolutionary Programming VII, 601–610. doi:

Eberhart, R. C., Shi, Y. (1998). Comparison between genetic algorithms and particle swarm optimization. Evolutionary Programming VII, 611–616. doi:

Pawar, P. M., Ganguli, R. (2007). Genetic fuzzy system for online structural health monitoring of composite helicopter rotor blades. Mechanical Systems and Signal Processing, 21 (5), 2212–2236. doi:

Chu, B., Kim, D., Hong, D., Park, J., Chung, J. T., Chung, J.-H., Kim, T.-H. (2008). GA-based fuzzy controller design for tunnel ventilation systems. Automation in Construction, 17 (2), 130–136. doi:

Franke, C., Hoffmann, F., Lepping, J., Schwiegelshohn, U. (2008). Development of scheduling strategies with Genetic Fuzzy systems. Applied Soft Computing, 8 (1), 706–721. doi:

Tang, J., Zhang, G., Lin, B., Zhang, B. (2010). A Hybrid PSO/GA Algorithm for Job Shop Scheduling Problem. Advances in Swarm Intelligence, 566–573. doi:

Robinson, J., Sinton, S., Rahmat-Samii, Y. (2002). Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna. IEEE Antennas and Propagation Society International Symposium (IEEE Cat. No.02CH37313). doi:

Dziwinski, P., Bartczuk, L. (2020). A New Hybrid Particle Swarm Optimization and Genetic Algorithm Method Controlled by Fuzzy Logic. IEEE Transactions on Fuzzy Systems, 28 (6), 1140–1154. doi:

Ruan, X., Wang, J., Zhang, X., Liu, W., Fu, X. (2020). A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients. Applied Sciences, 10 (10), 3352. doi:

Gao, W., Liu, S. (2012). A modified artificial bee colony algorithm. Computers & Operations Research, 39 (3), 687–697. doi:

Xue, Y., Jiang, J., Zhao, B., Ma, T. (2017). A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft Computing, 22 (9), 2935–2952. doi:




How to Cite

Afathi, M. (2021). Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems. Eastern-European Journal of Enterprise Technologies, 6(4 (114), 6–14.



Mathematics and Cybernetics - applied aspects