General Particle Swarm Optimization Research Articles

Detection of maximum loadability limits and weak buses using Chaotic PSO considering security constraints

In the current research chaotic search is used with the optimization technique for solving non-linear complicated power system problems because Chaos can overcome the local optima problem of optimization technique. Power system problem, more specifically voltage stability, is one of the practical examples of non-linear, complex, convex problems. Smart grid, restructured energy system and socio-economic development fetch various uncertain events in power systems and the level of uncertainty increases to a great extent day by day. In this context, analysis of voltage stability is essential. The efficient method to assess the voltage stability is maximum loadability limit (MLL). MLL problem is formulated as a maximization problem considering practical security constraints (SCs). Detection of weak buses is also important for the analysis of power system stability. Both MLL and weak buses are identified by PSO methods and FACTS devices can be applied to the detected weak buses for the improvement of stability. Three particle swarm optimization (PSO) techniques namely General PSO (GPSO), Adaptive PSO (APSO) and Chaotic PSO (CPSO) are presented for the comparative study with obtaining MLL and weak buses under different SCs. In APSO method, PSO-parameters are made adaptive with the problem and chaos is incorporated in CPSO method to obtain reliable convergence and better performances. All three methods are applied on standard IEEE 14 bus, 30 bus, 57 bus and 118 bus test systems to show their comparative computing effectiveness and optimization efficiencies.

Stochastic Stability Analysis of the Linear Continuous and Discrete PSO Models

Particle swarm optimization (PSO) can be interpreted physically as a particular discretization of a stochastic damped mass-spring system. Knowledge of this analogy has been crucial to derive the PSO continuous model and to introduce different PSO family members including the generalized PSO (GPSO) algorithm, which is the generalization of PSO for any time discretization step. In this paper, we present the stochastic analysis of the linear continuous and generalized PSO models for the case of a stochastic center of attraction. Analysis of the GPSO second order trajectories is performed and clarifies the roles of the PSO parameters and that of the cost function through the algorithm execution: while the PSO parameters mainly control the eigenvalues of the dynamical systems involved, the mean trajectory of the center of attraction and its covariance functions with the trajectories and their derivatives (or the trajectories in the near past) act as forcing terms to update first and second order trajectories. The similarity between the oscillation center dynamics observed for different kinds of benchmark functions might explain the PSO success for a broad range of optimization problems. Finally, a comparison between real simulations and the linear continuous PSO and GPSO models is shown. As expected, the GPSO tends to the continuous PSO when time step approaches zero. Both models account fairly well for the dynamics (first and second order moments) observed in real runs. This analysis constitutes so far the most realistic attempt to better understand and approach the real PSO dynamics from a stochastic point of view.

Generalized particle swarm optimization algorithm - Theoretical and empirical analysis with application in fault detection

A generalization of the particle swarm optimization (PSO) algorithm is presented in this paper. The novel optimizer, the Generalized PSO (GPSO), is inspired by linear control theory. It enables direct control over the key aspects of particle dynamics during the optimization process. A detailed theoretical and empirical analysis is presented, and parameter-tuning schemes are proposed. GPSO is compared to the classical PSO and genetic algorithm (GA) on a set of benchmark problems. The results clearly demonstrate the effectiveness of the proposed algorithm. Finally, an application of the GPSO algorithm to the fine-tuning of the support vector machines classifier for electrical machines fault detection is presented.

Graph partitioning by multi-objective real-valued metaheuristics: A comparative study

Abstract The graph partitioning is usually tackled as a single-objective optimization problem. Moreover, various problem-specific versions of different algorithms are proposed for solving this integer-valued problem, thus confusing practitioners in selecting an effective algorithm for their instances. On the other hand, although various metaheuristics are currently in great consideration towards different problem-domains, these are yet to be investigated widely to this problem. In this article, a novel attempt is made to investigate whether some common and established metaheuristics can directly be applied to different search spaces, instead of going through various problem-specific algorithms. For this, some mechanisms are proposed for handling the graph partitioning problem by general multi-objective real-valued genetic algorithm, differential evolution, and particle swarm optimization. Some algorithmic modifications are also proposed for improving the performances of the metaheuristics. Finally, the performances of the metaheuristics are compared in terms of their computer memory requirements, as well as their computational runtime and solution qualities based on some test cases with up to five objectives.

Hybrid Discrete Particle Swarm Algorithm for Graph Coloring Problem

Graph coloring problem (GCP) is one of the most studied combinatorial optimization problems. It is important in theory and practice. There have been many algorithms for graph coloring problem, including exact algorithms and (meta-)heuristic algorithms. In this paper, we attempt another meta-heuristic method ¾ particle swarm optimization to graph coloring problem. Particle swarm optimization (PSO) was originally developed for continuous problem. To apply PSO to discrete problem, the standard arithmetic operators of PSO are required to be redefined over discrete space. A conception of distance over discrete solution space is introduced. Under this notion of distance, the PSO operators are redefined. After reinterpreting the composition of velocity of a particle, a general discrete PSO algorithm is proposed. In order to solve graph coloring problem by the discrete PSO algorithm, an algorithm to implement the crucial PSO operator ¾ difference of two positions (solutions) is design ed . Then, a hybrid discrete PSO algorithm for graph coloring problem is propose d by combining a local search. Empirical study of the proposed hybrid algorithm is also carried out based on the second DIMACS challenge benchmarks. The experimental results are competitive with that of other well-known algorithms .

General Particle Swarm Optimization Algorithm for Integration of Process Planning and Scheduling

To realize the integration of process planning and scheduling (IPPS) in the manufacturing system, a particle swarm optimization (PSO) algorithm is utilized. Based on the general PSO (GPSO) model, one GPSO algorithm is projected to solve IPPS. In GPSO, crossover and mutation operations of genetic algorithm are respectively used for particles to exchange information and search randomly, and tabu search (TS) is used for particles’ local search. And time varying crossover probability and time varying maximum step size of tabu search are introduced. Experimental results show that IPPS can be solved by GPSO effectively. The feasibility of the proposed GPSO model and the significance of the research on IPPS are also demonstrated.

Application of a particle swarm optimization algorithm for determining optimum well location and type

Determining the optimum type and location of new wells is an essential component in the efficient development of oil and gas fields. The optimization problem is, however, demanding due to the potentially high dimension of the search space and the computational requirements associated with function evaluations, which, in this case, entail full reservoir simulations. In this paper, the particle swarm optimization (PSO) algorithm is applied for the determination of optimal well type and location. The PSO algorithm is a stochastic procedure that uses a population of solutions, called particles, which move in the search space. Particle positions are updated iteratively according to particle fitness (objective function value) and position relative to other particles. The general PSO procedure is first discussed, and then the particular variant implemented for well optimization is described. Four example cases are considered. These involve vertical, deviated, and dual-lateral wells and optimization over single and multiple reservoir realizations. For each case, both the PSO algorithm and the widely used genetic algorithm (GA) are applied to maximize net present value. Multiple runs of both algorithms are performed and the results are averaged in order to achieve meaningful comparisons. It is shown that, on average, PSO outperforms GA in all cases considered, though the relative advantages of PSO vary from case to case. Taken in total, these findings are very promising and demonstrate the applicability of PSO for this challenging problem.

1226 動的環境を考慮したParticle Swarm Optimizationに基づく非線形制御系設計(GS-10 先端制御)

1226 動的環境を考慮したParticle Swarm Optimizationに基づく非線形制御系設計(GS-10 先端制御)

The Generalized PSO: A New Door to PSOEvolution

A generalized form of the particle swarm optimization (PSO) algorithm is presented. Generalized PSO (GPSO) is derived from a continuous version of PSO adopting a time step different than the unit. Generalized continuous particle swarm optimizations are compared in terms of attenuation and oscillation. The deterministic and stochastic stability regions and their respective asymptotic velocities of convergence are analyzed as a function of the time step and the GPSO parameters. The sampling distribution of the GPSO algorithm helps to study the effect of stochasticity on the stability of trajectories. The stability regions for the second‐, third‐, and fourth‐order moments depend on inertia, local, and global accelerations and the time step and are inside of the deterministic stability region for the same time step. We prove that stability regions are the same under stagnation and with a moving center of attraction. Properties of the second‐order moments variance and covariance serve to propose some promising parameter sets. High variance and temporal uncorrelation improve the exploration task while solving ill‐posed inverse problems. Finally, a comparison is made between PSO and GPSO by means of numerical experiments using well‐known benchmark functions with two types of ill‐posedness commonly found in inverse problems: the Rosenbrock and the “elongated” DeJong functions (global minimum located in a very flat area), and the Griewank function (global minimum surrounded by multiple minima). Numerical simulations support the results provided by theoretical analysis. Based on these results, two variants of Generalized PSO algorithm are proposed, improving the convergence and the exploration task while solving real applications of inverse problems.

Self-generation RBFNs using evolutional PSO learning

In this paper, a powerful evolutional particle swarm optimization (PSO) learning algorithm is developed that self-generates radial basis function neural networks (RBFNs) to deal with three nonlinear problems. In general, the simple but efficient PSO learning algorithm is adapted for solving complex and global optimization problems. With computer simulation, this paper illustrates, in detail, how the PSO algorithm can automatically tune the centers and spreads of each radial basis function, and the connection weights. Furthermore, in parallel with the free RBFNs parameters, the number of radial basis functions of the constructed RBFNs can be automatically minimized by choosing a special fitness function which takes this factor into account. Therefore, the proposed PSO allows high accuracy within a short training time when determining RBFNs with small numbers of radial basis functions. Simulation results demonstrate the proposed RBFNs’ efficiency to stabilize an inverted pendulum, approximate a nonlinear function and approximate a discrete dynamic system.