Metaheuristics for Continuous and Combinatorial Optimization Problems
Computer Science and Technology
College of Computer Science and Electronic Engineering
In this thesis, we propose efficient and effective algorithms for solving continuous and combinatorial optimization problems. Besides, the main contributions of this thesis can be briefly summarized as follows:
(1)The author gives a detailed synopsis of the metaheuristic optimization algorithms related to this study which are: Chemical reaction optimization (CRO), differential evolution (DE) and artificial bee colony (ABC).
(2)The author proposed a hybrid algorithm called HP-CRO-DE2 that combine chemical reaction optimization and differential evolution methods to solve global numerical optimization problems. Besides, we inject two mutation schemes issued from DE into CRO to improve the diversity and the exploitation of the algorithm. Experimental results show the performance of the proposed HP-CRO-DE2 comparing with the other existing algorithms.
(3)The author proposed new binary artificial named NB-ABC to solve binary optimization problems. Besides, new search mechanisms have been designed for the employed, the onlooker and the scout bee phases. More precisely, new binary search operators using the well-known Boolean operators such as OR, XOR and complement are developed to improve the exploitation and exploration capabilities of the algorithm. The experimental results demonstrate the efficiency of the proposed NB-ABC algorithm.
 Nouioua Mourad, Zhiyong Li. (2017). Using differential evolution strategies in chemical reaction optimization for global numerical optimization. Applied Intelligence. Volume 47(3). Pages 935-961.  Nouioua Mourad, Zhiyong Li, Shilong Jiang (2018). New Binary Artificial Bee Colony for the 0-1 Knapsack Problem. In: Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science. Volume 10941. Pages 153-165.
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