Particle swarm optimization method

The performance of the fuzzy control system can be further enhanced by fine-tunning the parameters of the fuzzy system (membership functions, rules, etc) by using a neural network or a global optimization scheme. First results in this direction, using the Particle Swarm Optimization method, have been presented in Marinakis et al. 2008. Furthermore, from the practical point of view a big deficiency of fuzzy controllers are... 

Ordaz, M. 1988. On the use of probability concentrations. Structural Safety 5, 317-318. Parsopoulos, K.E. Vrahatis, M.N. 2001. Particle swarm optimization method for constrained optimization problems. Technical report. Department of Mathematics, University of Patras, Greece. 

The optimal damper distributions in buildings are found for various objective functions. The weighted sum of amplitudes of the transfer functions of interstorey drifts and the weighted sum of amplitudes of the transfer functions of displacements evaluated at the fundamental natural frequency of the frame with the dampers are most frequently used as the objective function. The optimization problem is solved using the sequential optimization method and the particle swarm optimization method. Several numerical solutions to the considered optimization problem are presented and discussed in detail. 

Fig. 9 Convergence of particle swarm optimization method to one point in the Pareto set...

AI methods may be used in various ways. The models may be used as a standalone application, e.g., in recent work on the design of microwave absorbers using particle swarm optimization (PSO).6 Alternatively, a computational tool, such as a finite element analysis or a quantum mechanical calculation, may be combined with an AI technique, such as an evolutionary algorithm. 

Abstract. In the present paper the problem of reuse water networks (RWN) have been modeled and optimized by the application of a modified Particle Swarm Optimization (PSO) algorithm. A proposed modified PSO method lead with both discrete and continuous variables in Mixed Integer Non-Linear Programming (MINLP) formulation that represent the water allocation problems. Pinch Analysis concepts are used jointly with the improved PSO method. Two literature problems considering mono and multicomponent problems were solved with the developed systematic and results has shown excellent performance in the optimality of reuse water network synthesis based on the criterion of minimization of annual total cost. 

Some well-known stochastic methods for solving SOO problems are simulated annealing (SA), GAs,DE and particle swarm optimization (PSO). These were initially proposed and developed for optimization problems with bounds only [that is, unconstrained problems without Equations (4.7) and (4.8)]. Subsequently, they were extended to constrained problems by incorporating a strategy for handling constraints. One relatively simple and popular sdategy is the penalty function, which involves modifying the objective function (Equation 4.5) by the addition (in the case of minimization) of a term which depends on constraint violation. Eor example, see Equation (4.9),... 

Instead of transforming a MOO problem into a SOO problem and solving it repeatedly, researchers have modified stochastic SOO methods for solving MOO problems. MOO methods such as NSGA-n, I-MODE and Multi-objective Particle Swarm optimization (MOPSO) can generate many Pareto-optimal solutions in a single run even for problems with many... 

The PSO has many variants. The Repulsive Particle Swarm (RPS) method of optimization, one of such variants and appHed in the presented method, is particularly effective in finding out the global optimmn in very complex search spaces (although it may be slower on certain types of optimization problems). 

Method for optimization of the maintenance activities in the nuclear power plant is presented. The optimization is done with the apphcation of the modified particle swarm optimization algorithm. The safety of the system is assessed throng the mean value of the system unavailabihty calculated for discrete time points. 

The developed method was apphed on test system representing simplified high pressure injection system of the nuclear power plant. The comparison of the obtained results with the results of other two optimization algorithms is done. The obtained results show that particle swarm optimization algorithm surpass other optimization algorithms by its speed and optimal function value. 

A method for optimizing the control parameters, which are normally decided without regard to the uncertainties implied in the system. In present research use was made of the method of particle swarm optimization (Kennedy and Eberhart 2001). 

In recent years several optimization techniques inspired in biological processes have been proposed. This comprises genetic algorithms, evolution strategies, ant-colony optimization, particle swarm optimization and others. In the research reported herein the last method has been adopted, as limited research indicates it presents advantages over other methods. 

Pongchairerks P, Kachitvichyanukul V (2009) Particle swarm optimization algorithm with multiple social learning structures. Int J Oper Res 6(2) 176-194 Roodbergen KJ, Koster R (2001) Routing methods for warehouses with multiple cross aisles. Int J Prod Res 39(9) 1865-1883... 

This article particularly will build the CLSC model and solution suggestions based on particle swarm optimization (PSO) algorithm, a heuristic global optimization method. PSO was proposed by Eberhart and Kennedy (1995), and they claimed each particle movement is guided by their own best known position in the search space as well as the entire swarm s best known position. Many studies have used PSO to solve the NP-hard linear programming problems. 

Particle Swarm Optimization (PSO) The optimization method based on the study of social behaviour in a self-organized population system (i.e., ant colonies, fish schools). Itisanon-gradient, heuristic method which requires calculation of the objective function only. This method is able to find a global solution to non-convex optimization problem and problems which have many local minima. 

Particle Swarm Optimization (PSO) is a stochastic optimization method evolved from Swarm Theory and Evolutionary Computation [4]. It is instigated by animals natural swarming behavior [5]. PSO has been proven to be a suitable technique for solving various optimization problems [6, 7]. Among the advantages of PSO are that it allows efficient and rapid optimization of the problem, due to its parallel nature, it requires only basic mathematical operators for optimization and it provides low computational and memory costs for each iteration [8]. Many variants of the PSO algorithm exist, such as PSO with inertia weight [9], PSO with constriction factor [10], and mutative PSO [11]. 

I. M. Yassin, N. M. Tahir, M. K. M. Salleh, H. A. Hassan, A. Zabidi, and H. Z. Abidin, "Novel Mutative Particle Swarm Optimization Algorithm for Discrete Optimization in The 2009 International Conference on Genetic and Evolutionary Methods (GEM09), Las Vegas, NV,2009,pp. 137-142. 

In addition to the two major approaches described above, there are several other methods or variants, either for generally purpose or designed for specific systems. Here we give an introduction of topological methods, particle swarm optimization and tabu search which have been combined with first-principles calculations recently for global optimization of clusters. 

The same author [88], obtained a model based on artificial neural network with particle swarm optimization using as inpnit the molar mass and the structure of the compoimd, represented by the abundance of defined fragments. This method was valid for imidazolium based ILs over a wide range of pressures and temperatures. Using a back propagation neural-network Lazzus[89] estimated the density of several families of ILs using as input the frequency or specific fragments, temperature, pressure and molar mass. 

---