Continuous Particle Swarm Optimization Algorithm Research Articles

Application of a Continuous Particle Swarm Optimization (CPSO) for the Optimal Coordination of Overcurrent Relays Considering a Penalty Method

In an electrical power system, the coordination of the overcurrent relays plays an important role in protecting the electrical system by providing primary as well as backup protection. To reduce power outages, the coordination between these relays should be kept at the optimum value to minimize the total operating time and ensure that the least damage occurs under fault conditions. It is also imperative to ensure that the relay setting does not create an unintentional operation and consecutive sympathy trips. In a power system protection coordination problem, the objective function to be optimized is the sum of the total operating time of all main relays. In this paper, the coordination of overcurrent relays in a ring fed distribution system is formulated as an optimization problem. Coordination is performed using proposed continuous particle swarm optimization. In order to enhance and improve the quality of this solution a local search algorithm (LSA) is implanted into the original particle swarm algorithm (PSO) and, in addition to the constraints, these are amalgamated into the fitness function via the penalty method. The results achieved from the continuous particle swarm optimization algorithm (CPSO) are compared with other evolutionary optimization algorithms (EA) and this comparison showed that the proposed scheme is competent in dealing with the relevant problems. From further analyzing the obtained results, it was found that the continuous particle swarm approach provides the most globally optimum solution.

A novel method for optimum fault current limiter placement using particle swarm optimization algorithm

Summary Connection of distributed generation to the grid and the expansion of transmission system in order to meet the growing for electricity are causing the extent and complexity of the network structure. The occurrence of fault in such networks leads to flow large short circuit currents through the system, which may exceed the rating of existing circuit breakers and can damage system equipment. The utilization of fault current limiters (FCLs) in power systems can be an effective method to limit fault currents. FCLs can offer many benefits, but these benefits depend on the number, installation location, and impedance of FCL. In this paper, determining the number, location, and impedance of FCLs in network is modeled as a new optimization problem when objective functions are reliability, power loss, and economical use of FCLs. Also, a combination of discrete and continuous particle swarm optimization algorithm is employed to solve problem. Modified RBTS 2 bus test system is considered to evaluate the effectiveness and feasibility of the proposed method. Obtained results show the importance of finding optimum number, location, and impedance of FCLs in minimization of the proposed objective functions. Copyright © 2014 John Wiley & Sons, Ltd.

An Ant Colony Optimization Algorithm for Uncapacitated Facility Location Problem

An ant colony optimization (ACO) is employed to solve uncapacitated facility location (UFL) problem, which is one of the most widely studied in combinatorial optimization. This is the first approach to solve this problem using ACO technique. Various exact algorithms like branch and bound, linear programming, Lagrangean relaxation algorithms had already been used to solve the problem. On the other hand the meta heuristic approach like continuous particle swarm optimization algorithm (CPSO) [1] has been found very useful to solve this problem. The same benchmark suites that were used in CPSO algorithm collected from OR-library [2] are applied here.

Optimizing Irrigation Water Allocation and Multicrop Planning Using Discrete PSO Algorithm

AbstractThis study presents a linear and a mixed-integer linear (MIL) model for optimizing an irrigation water allocation and a multicrop planning problem. The main objective was to maximize net benefit for all cultivated crops within irrigated areas in a reservoir-irrigation system in Iran. The linear model was optimized with linear programming (LP) method and continuous particle swarm optimization (CPSO) algorithm to make a detailed comparison between the LP method and CPSO algorithm results. The optimal solution obtained by the CPSO algorithm and LP method in the linear model were comparable. However, the optimal allocated areas for both crops and orchards in the linear model obtained by the LP method and CPSO algorithm were not directly applicable in real crop planning situations. Consequently, the MIL model was developed for which a discrete particle swarm optimization (DPSO) algorithm was used to obtain an applicable and allowable solution for the problem. Contrary to LP and CPSO, the DPSO algorithm...

A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem

A discrete version of particle swarm optimization (DPSO) is employed to solve uncapacitated facility location (UFL) problem which is one of the most widely studied in combinatorial optimization. In addition, a hybrid version with a local search is defined to get more efficient results. The results are compared with a continuous particle swarm optimization (CPSO) algorithm and two other metaheuristics studies, namely, genetic algorithm (GA) and evolutionary simulated annealing (ESA). To make a reasonable comparison, we applied to same benchmark suites that are collected from OR-library. In conclusion, the results showed that DPSO algorithm is slightly better than CPSO algorithm and competitive with GA and ESA.

PSO-based algorithm for home care worker scheduling in the UK

This paper presents the novel application of a collaborative population-based meta-heuristic technique called Particle Swarm Optimization (PSO) to the scheduling of home care workers. The technique is applied to a genuine situation arising in the UK, where the provision of community care service is a responsibility of the local authorities. Within this provision, optimization routes for each care worker are determined in order to minimize the distance traveled providing that the capacity and service time window constraints are not violated. The objectives of this paper are twofold; first to exploit a systematic approach to improve the existing schedule of home care workers, second to develop the methodology to enable the continuous PSO algorithm to be efficiently applied to this type of problem and all classes of similar problems. For this problem, a particle is defined as a multi-dimensional point in space which represents the corresponding care activities and assignment priority. The Heuristic Assignment scheme is specially designed to transform the continuous PSO algorithm to the discrete job schedule. The Earliest Start Time Priority with Minimum Distance Assignment (ESTPMDA) technique is developed for generating an initial solution which guides the search direction of the particle. Local improvement procedures (LIP), i.e. insertion and swap, are embedded in the PSO algorithm in order to further improve the solution quality. The proposed methodology is implemented, tested, and compared with existing solutions on a variety of real problem instances.

A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem

In this paper, a particle swarm optimization algorithm (PSO) is presented to solve the permutation flowshop sequencing problem (PFSP) with the objectives of minimizing makespan and the total flowtime of jobs. For this purpose, a heuristic rule called the smallest position value (SPV) borrowed from the random key representation of Bean [J.C. Bean, Genetic algorithm and random keys for sequencing and optimization, ORSA Journal of Computing 6(2) (1994) 154–160] was developed to enable the continuous particle swarm optimization algorithm to be applied to all classes of sequencing problems. In addition, a very efficient local search, called variable neighborhood search (VNS), was embedded in the PSO algorithm to solve the well known benchmark suites in the literature. The PSO algorithm was applied to both the 90 benchmark instances provided by Taillard [E. Taillard, Benchmarks for basic scheduling problems, European Journal of Operational Research, 64 (1993) 278–285], and the 14,000 random, narrow random and structured benchmark instances provided by Watson et al. [J.P. Watson, L. Barbulescu, L.D. Whitley, A.E. Howe, Contrasting structured and random permutation flowshop scheduling problems: Search space topology and algorithm performance, ORSA Journal of Computing 14(2) (2002) 98–123]. For makespan criterion, the solution quality was evaluated according to the best known solutions provided either by Taillard, or Watson et al. The total flowtime criterion was evaluated with the best known solutions provided by Liu and Reeves [J. Liu, C.R. Reeves, Constructive and composite heuristic solutions to the P∥∑ C i scheduling problem, European Journal of Operational Research 132 (2001) 439–452], and Rajendran and Ziegler [C. Rajendran, H. Ziegler, Ant-colony algorithms for permutation flowshop scheduling to minimize makespan/total flowtime of jobs, European Journal of Operational Research, 155(2) (2004) 426–438]. For the total flowtime criterion, 57 out of the 90 best known solutions reported by Liu and Reeves, and Rajendran and Ziegler were improved whereas for the makespan criterion, 195 out of the 800 best known solutions for the random and narrow random problems reported by Watson et al. were improved by the VNS version of the PSO algorithm.

A discrete version of particle swarm optimization for flowshop scheduling problems

Particle swarm optimization (PSO) is a novel metaheuristic inspired by the flocking behavior of birds. The applications of PSO to scheduling problems are extremely few. In this paper, we present a PSO algorithm, extended from discrete PSO, for flowshop scheduling. In the proposed algorithm, the particle and the velocity are redefined, and an efficient approach is developed to move a particle to the new sequence. To verify the proposed PSO algorithm, comparisons with a continuous PSO algorithm and two genetic algorithms are made. Computational results show that the proposed PSO algorithm is very competitive. Furthermore, we incorporate a local search scheme into the proposed algorithm, called PSO-LS. Computational results show that the local search can be really guided by PSO in our approach. Also, PSO-LS performs well in flowshop scheduling with total flow time criterion, but it requires more computation times.