An efficient multi-objective gorilla troops optimizer for minimizing energy consumption of large-scale wireless sensor networks. (February 2023)

• Record Type: Journal Article
• Authors: Houssein, Essam H.; Saad, Mohammed R.; Ali, Abdelmgeid A.; Shaban, Hassan
• Is Part Of: Expert systems with applications. Volume 212(2023)
• Publication Date: 2023-02
• Subjects: Gorilla troops optimizer -- Multi-objective optimization -- Pareto optimal alternatives -- CEC'2020 test suite -- Large-scale wireless sensor networks; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118827
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: The multi-objective gorilla troops optimizer (MOGTO) is a new version of the gorilla troops optimizer (GTO) proposed in this paper to address multi-objective optimization issues. The Pareto optimum solutions acquired by the GTO are saved in an external archive. In the multi-objective search region, the archive was used to mimic the gorilla groups' collective behavior. The suggested approach is evaluated statistically and qualitatively in solving various multi-objective issues using the congress on evolutionary computation (CEC) 2020 test bed. In large-scale wireless sensor networks, the proposed algorithm is also utilized to discover the minimal number of sink nodes with the lowest localization error, which will cap the whole network and increase the network lifespan. Meanwhile, the multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm version 2 (NSGA-II), multi-objective grey wolf optimizer (MOGWO), multi-objective whale optimization algorithm (MOWOA), multi-objective sine–cosine algorithm (MOSCA), multi-objective slime mould algorithm (MOSMA), multi-objective particle swarm optimization with ring topology and special crowding distance (MO_Ring_PSO_SCD), hybrid NSGAII-MOPSO, multi-objective evolutionary algorithm based on decomposition (MOEA/D), and improved multi-objective manta ray foraging optimization (IMOMRFO) are the ten familiar and strong optimization models, which are compared with the proposed algorithm. Simulation results in CEC'2020 test functions indicated that the proposed MOGTO can provide remarkable results than other optimization models in terms of Pareto set proximity (PSP), inverted generational distance in decision space (IGDX), and hyper volume (HV) indicators. Additionally, simulation results in large-scale wireless sensor networks show that the proposed algorithm can discover the smallest number of sink nodes and diminish the network's energy usage. Highlights: An efficient MOGTO algorithm is proposed based on NDS and CD techniques. CEC 2020 test suite is utilized for verification of MOGTO performance. MOGTO is proposed for minimizing energy consumption of LSWSNs. MOGTO is analyzed using various analysis metrics. The performance of the MOGTO is better than other competitor algorithms.