Authors:
Chnini Samia
1
;
Abadlia Houda
2
;
Smairi Nadia
3
and
Nasri Nejah
1
Affiliations:
1
SETIT Laboratory, Faculty of Sciences of Gafsa, University of Gafsa, Tunisia
;
2
Univ. Manouba, ENSI, LARIA UR22ES01, Campus Universitaire Manouba, Tunisia
;
3
Nadia Smairi, COSMOS Laboratory, National School of Computer Sciences, University of Manouba, Tunisia
Keyword(s):
Many-Objective Optimization, Distributed Optimization, Island Model.
Abstract:
With the growing complexity of agricultural systems and the need to optimize multiple conflicting objectives simultaneously, traditional optimization methods often struggle to find satisfactory solutions. In this work, we introduce a novel enhancement to the standard Multi Objectives Particle Swarm Optimization (MOPSO) algorithm that significantly improves its effectiveness in handling the diverse and dynamic objectives inherent in agricultural optimization problems. we propose an improvement to the MOPSO algorithm by introducing an islanding technique to promote exploration and exploitation of the many-objective search space. The improved MOPSO algorithm, called I-MOPSO guide the search towards optimal and diverse solutions by dividing the search space into islands and facilitating information exchange between them. We put I-MOPSO into practice and tested it using a series of common many objective optimization algorithms. According to Experimental results show that I-MOPSO is capabl
e of finding high-quality solutions on a variety of test problems, often outperforming the standard MOPSO algorithm and NSGAIII.
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