Konstantinos E. Parsopoulos

Recent approaches to global optimization problems through Particle Swarm Optimization

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 ℓ1 errors-in-variables problems, as well as problems in noisy and continuously changing environments, are reported. Finally, a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given.

On the computation of all global minimizers through particle swarm optimization

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 of the particle swarm optimization (PSO) method, resulting in an efficient algorithm which has the ability to avoid previously detected solutions and, thus, detect all global minimizers of a function. Experimental results on benchmark problems originating from the fields of global optimization, dynamical systems, and game theory, are reported, and conclusions are derived.

Particle swarm optimization method in multiobjective problems

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 weights is considered. Furthermore, critical aspects of the VEGA approach for Multiobjective Optimization using Genetic Algorithms are adapted to the PSO framework in order to develop a multi-swarm PSO that can cope effectively with MO problems. Conclusions are derived and ideas for further research are proposed.

Particle Swarm Optimization and Intelligence: Advances and Applications

Particle Swarm Optimization and Intelligence: Advances and Applications examines modern intelligent optimization algorithms proven as very efficient in applications from various scientific and technological fields. Providing distinguished and unique research, this innovative publication offers a compendium of leading field experiences as well as theoretical analyses and complementary techniques useful to academicians and practitioners.

Particle swarm optimization for integer programming

The investigation of the performance of the particle swarm optimization (PSO) method in integer programming problems, is the main theme of the present paper. Three variants of PSO are compared with the widely used branch and bound technique, on several integer programming test problems. Results indicate that PSO handles efficiently such problems, and in most cases it outperforms the branch and bound technique.

Unified particle swarm optimization for solving constrained engineering optimization problems

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 with promising results. Comparisons with the standard local and global variant of Particle Swarm Optimization are reported and discussed.

Memetic particle swarm optimization

Abstract We propose a new Memetic Particle Swarm Optimization scheme that incorporates local search techniques in the standard Particle Swarm Optimization algorithm, resulting in an efficient and effective optimization method, which is analyzed theoretically. The proposed algorithm is applied to different unconstrained, constrained, minimax and integer programming problems and the obtained results are compared to that of the global and local variants of Particle Swarm Optimization, justifying the superiority of the memetic approach.

Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization

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 the experts. This is performed through the minimization of a properly defined objective function. This novel method overcomes some deficiencies of other learning algorithms and, thus, improves the efficiency and robustness of Fuzzy Cognitive Maps. The operation of the new method is illustrated on an industrial process control problem, and the obtained simulation results support the claim that it is robust and efficient.

Parameter selection and adaptation in Unified Particle Swarm Optimization

The performance of the recently proposed Unified Particle Swarm Optimization method is investigated under different schemes for the determination and adaptation of the unification factor, which is the main parameter of the method, controlling its exploration and exploitation properties. Widely used benchmark problems are employed and numerous experiments are conducted along with statistical tests to yield useful conclusions regarding the effect of the parameter on the algorithms performance as well as the most efficient adaptation schemes.

Modification of the Particle Swarm Optimizer for Locating All the Global Minima

In many optimization applications, escaping from the local minima as well as computing all the global minima of an objective function is of vital importance. In this paper the Particle Swarm Optimization method is modified in order to locate and evaluate all the global minima of an objective function. The new approach separates the swarm properly when a candidate minimizer is detected. This technique can also be used for escaping from the local minima which is very important in neural network training.