Konstantinos E. Parsopoulos

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This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting multiple minimizers are described. Moreover, results on the ability of the PSO in tackling Multiobjective, Minimax, Integer Programming and 1 errors-in-variables problems, as(More)
The performance of the Particle Swarm Optimization method in coping with Constrained Optimization problems is investigated in this contribution. In the adopted approach a non{stationary multi{stage assignment penalty function is incorporated, and several experiments are performed on well{known and widely used benchmark problems. The obtained results are(More)
This paper constitutes a first study of the Particle Swarm Optimization (PSO) method in Multiobjective Optimization (MO) problems. The ability of PSO to detect Pareto Optimal points and capture the shape of the Pareto Front is studied through experiments on well-known non-trivial test functions. The Weighted Aggregation technique with fixed or adaptive(More)
This paper studies a parallel version of the Vector Evaluated Particle Swarm Optimization (VEPSO) method for multiobjective problems. Experiments on well known and widely used test problems are performed, aiming at investigating both the efficiency of VEPSO as well as the advantages of the parallel implementation. The obtained results are compared with the(More)
This paper introduces a new learning algorithm for Fuzzy Cognitive Maps, which is based on the application of a swarm intelligence algorithm, namely Particle Swarm Optimization. The proposed approach is applied to detect weight matrices that lead the Fuzzy Cognitive Map to desired steady states, thereby refining the initial weight approximation provided by(More)
We propose a highly flexible sequential methodology for the experimental analysis of optimization algorithms. The proposed technique employs computational statistic methods to investigate the interactions among optimization problems, algorithms, and environments. The workings of the proposed technique are illustrated on the parameterization and comparison(More)
We investigate the performance of the recently proposed Unified Particle Swarm Optimization method on constrained engineering optimization problems. For this purpose, a penalty function approach is employed and the algorithm is modified to preserve feasibility of the encountered solutions. The algorithm is illustrated on four well–known engineering problems(More)