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)
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 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 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)
— A parallel, multi–population Differential Evolution algorithm for multiobjective optimization is introduced. The algorithm is equipped with a domination selection operator to enhance its performance by favoring non–dominated individuals in the populations. Preliminary experimental results on widely used test problems are promising. Comparisons with the(More)
We propose a self–adaptive probabilistic neural network model, which incorporates optimization algorithms to determine its spread parameters. The performance of the proposed model is investigated on two protein localization problems, as well as on two medical diagnostic tasks. Experimental results are compared with that of feedforward neural networks and(More)
— The computation of periodic orbits of nonlinear mappings is very important for studying and better understanding the dynamics of complex systems. Evolutionary algorithms have shown to be an efficient alternative for the computation of periodic orbits in cases where the inherent properties of the problem at hand render gradient–based methods invalid. Such(More)
High-dimensional optimization problems appear very often in demanding applications. Although evolutionary algorithms constitute a valuable tool for solving such problems, their standard variants exhibit deteriorating performance as dimension increases. In such cases, cooperative approaches have proved to be very useful, since they divide the computational(More)
—This paper presents approaches for effectively computing all global minimizers of an objective function. The approaches include transformations of the objective function through the recently proposed deflection and stretching techniques , as well as a repulsion source at each detected minimizer. The aforementioned techniques are incorporated in the context(More)