Ahmed Y. Hamed¹, Monagi H. Alkinani², M. R. Hassan^{3, *}

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1159-1173, 2020, DOI:10.32604/cmc.2020.09176

Abstract In the distributed networks, many applications send information from a source node to multiple destination nodes. To support these applications requirements, the paper presents a multi-objective algorithm based on ant colonies to construct a multicast tree for data transmission in a computer network. The proposed algorithm simultaneously optimizes total weight (cost, delay and hop) of the multicast tree. Experimental results prove the proposed algorithm outperforms a recently published Multi-objective Multicast Algorithm specially designed for solving the multicast routing problem. Also, it is able to find a better solution with fast convergence speed and high reliability. More >

Qi Wang^1,*, Wei Liu¹, Hualong Yu¹, Shang Zheng¹, Shang Gao¹, Fabrizio Granelli²

CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.1, pp. 83-103, 2019, DOI:10.32604/cmes.2019.06897

Abstract The wireless sensor network (WSN) is widely employed in the application scenarios of the Internet of Things (IoT) in recent years. Extending the lifetime of the entire system had become a significant challenge due to the energy-constrained fundamental limits of sensor nodes on the perceptual layer of IoT. The clustering routing structures are currently the most popular solution, which can effectively reduce the energy consumption of the entire network and improve its reliability. This paper introduces an enhanced hybrid intelligential algorithm based on particle swarm optimization (PSO) and ant colony optimization (ACO) method. The enhanced PSO is deployed to select… More >

F. Aymerich¹, M. Serra²

CMES-Computer Modeling in Engineering & Sciences, Vol.13, No.1, pp. 49-66, 2006, DOI:10.3970/cmes.2006.013.049

Abstract The study reported in this paper explores the potential of Ant Colony Optimization (ACO) metaheuristic for stacking sequence optimization of composite laminates. ACO is a recently proposed population-based search approach able to deal with a wide range of optimization problems, especially of a combinatorial nature, and inspired by the natural foraging behavior of ant colonies. ACO search processes, in which the activities of real ants are simulated by means of artificial agents that communicate and cooperate through the modification of the local environment, were implemented in a specifically developed numerical algorithm aimed at the lay-up optimization (based on a strain… More >