Browsing by Author "Siarry, Patrick"

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    Convergence proof of an enhanced particle swarm optimisation method integrated with evolutionary game theory
    (Elsevier, 2016-01-08) Leboucher, Cedric; Shin, Hyosang; Siarry, Patrick; Le Menec, Stephanie; Chelouah, Rachid; Tsourdos, Antonios
    This paper proposes an enhanced Particle Swarm Optimisation (PSO) algorithm and examines its performance. In the proposed PSO approach, PSO is combined with Evolutionary Game Theory to improve convergence. One of the main challenges of such stochastic optimisation algorithms is the difficulty in the theoretical analysis of the convergence and performance. Therefore, this paper analytically investigates the convergence and performance of the proposed PSO algorithm. The analysis results show that convergence speed of the proposed PSO is superior to that of the Standard PSO approach. This paper also develops another algorithm combining the proposed PSO with the Standard PSO algorithm to mitigate the potential premature convergence issue in the proposed PSO algorithm. The combined approach consists of two types of particles, one follows Standard PSO and the other follows the proposed PSO. This enables exploitation of both diversification of the particles’ exploration and adaptation of the search direction.
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    An enhanced particle swarm optimization method integrated with evolutionary game theory
    (IEEE, 2018-01-03) Leboucher, Cédric; Shin, Hyosang; Chelouah, Rachid; Le Ménec, Stéphane; Siarry, Patrick; Formoso, Mathias; Tsourdos, Antonios; Kotenkoff, Alexandre
    This paper describes a novel particle swarm optimizer algorithm. The focus of this study is how to improve the performance of the classical particle swarm optimization approach, i.e., how to enhance its convergence speed and capacity to solve complex problems while reducing the computational load. The proposed approach is based on an improvement of particle swarm optimization using evolutionary game theory. This method maintains the capability of the particle swarm optimizer to diversify the particles' exploration in the solution space. Moreover, the proposed approach provides an important ability to the optimization algorithm, that is, adaptation of the search direction, which improves the quality of the particles based on their experience. The proposed algorithm is tested on a representative set of continuous benchmark optimization problems and compared with some other classical optimization approaches. Based on the test results of each benchmark problem, its performance is analyzed and discussed.
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    A two-step optimisation method for dynamic weapon target assignment problem
    (Intechopen, 2013-01-30) Leboucher, Cédric; Shin, Hyo-Sang; Siarry, Patrick; Chelouah, Rachid; Le Ménec, Stéphane; Tsourdos, Antonios
    The weapon target assignment (WTA) problem has been designed to match the Command & Control (C2) requirement in military context, of which the goal is to find an allocation plan enabling to treat a specific scenario in assigning available weapons to oncoming targets. The WTA always get into situation weapons defending an area or assets from an enemy aiming to destroy it. Because of the uniqueness of each situation, this problem must be solved in real-time and evolve accordingly to the aerial/ground situation. By the past, the WTA was solved by an operator taking all the decisions, but because of the complexity of the modern warfare, the resolution of the WTA in using the power of computation is inevitable to make possible the resolution in real time of very complex scenarii involving different type of targets. Nowadays, in most of the C2 this process is designed in order to be as a support for a human operator and in helping him in the decision making process. The operator will give its final green light to proceed the intervention.