Comparison of Evolutionary and Swarm Intelligence-based Approaches in the Improvement of Peach Fruit Quality

May 18, 2014 Posted by admin

A. Kadrani, B. Quilot-Turion, M. Génard, F. Lescourret and
M-M. Ould Sidi

INSEA, BP 6217 Rabat-Institus, 10106 Rabat, Maro
INRA, UR1052 Génétique et Amélioration des Fruits et Légumes, F-84143 Montfavet, France
INRA, R1115 Plantes et Systèmes de culture Horticoles, F-84914 Avignon, France

Abstract
The design of peach ideotypes that satisfy the requirement of high fruit quality and low sensitivity to fungal diseases in a given environment is a very challenging problem. In this paper, we propose a model-based design approach to deal with this challenge. First, we formulate it as a multi-objective optimization problem. Two well-known multi-objective optimization algorithms i.e. the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-Objective Particle Swarm Optimization with the Crowding Distance (MOPSO-CD) were then used to find the best combinations of genetic resources and cultural practices adapted to, and respectful of specific environments. Statistically significant performance measures are employed to compare the two algorithms. The results obtained demonstrate that NSGA-II is able to yield a wide spread of solutions with good coverage and convergence to Pareto fronts.

Keywords:Multi-objective optimization, NSGA-II, MOPSO-CD, decision-making, model-based design, peach ideotypes, brown rot, Virtual Fruit model.

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