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This paper presents the comparison of some well-known global optimization techniques in optimization of an expert system controlling a ship locking process. The purpose of the comparison is to find the best algorithm for optimization of membership function parameters of fuzzy expert system for the ship lock control. Optimization was conducted in order to achieve better results in local distribution of ship arrivals, i.e. shorter waiting times for ships and less empty lockages. Particle swarm optimization, artificial bee colony optimization and genetic algorithm were compared. The results shown in this paper confirmed that all these procedures show similar results and provide overall improvement of ship lock operation performance, which speaks in favour of their application in similar transportation problem optimization.

This paper presents a novel approach of designing linear phase FIR low pass and high pass filter using Random PSO in hybrid with DE known as Random PSODE (RPSODE). In this paper, the Random PSO is used which utilises the weighted particle to guide the search direction for both explorative and exploitative searches. Differential evolution (DE) is one of the very fast and robust evolutionary algorithms which has shown superior performance for continuous global optimization; uses differential information to guide its search direction but sometime causes instability problem; whereas, PSO is a robust, population based stochastic search technique but has the problem of sub-optimality. This paper efficiently combines the Random PSO and DE so as to overcome the disadvantages faced by both the algorithms individually and is used for the design of linear phase low pass and high pass FIR filters. The simulation results show the superiority of RPSODE in global convergence properties and local search ability, and prove it to be a promising candidate for designing the FIR filters. RPSODE outperforms PSO, DE, and PSODE not only in magnitude response but in the convergence speed as well.

In this paper, the new hybrid particle swarm optimization (hybrid-PSO) based on particle swarm optimization (PSO), evolutionary programming (EP), and tabu search (TS) is developed. Hybrid-PSO is proposed to determine the optimal allocation of multi-type flexible AC transmission system (FACTS) controllers for simultaneously maximizing the power transfer capability of power transactions between generators and loads in power systems without violating system constraints. The particular optimal allocation includes optimal types, locations, and parameter settings. Four types of FACTS controllers consist of thyristor-controlled series capacitor (TCSC), thyristor-controlled phase shifter (TCPS), static var compensator (SVC), and unified power flow controller (UPFC). Power transfer capability determinations are calculated based on optimal power flow (OPF) technique. Test results on IEEE RTS 24-bus system, IEEE 30-bus system and, IEEE 118-bus system indicate that optimally placed OPF with FACTS controllers by the hybrid-PSO could enhance the higher power transfer capability more than those from EP and conventional PSO.

Particle Swarm Optimization (PSO) algorithm has recently gained more attention in the global optimization research due to its simplicity and global search ability. This paper proposes a hybrid of PSO and Generalized Generation Gap model with Parent- Centric Recombination operator (G3PCX) [25], a well-known real-coded genetic algorithm. The proposed hybrid algorithm, namely PSPG, combines fast convergence and rotational invariance of G3PCX as well as global search ability of PSO. The performance of PSPG algorithm is evaluated using 8 widely-used nonlinear benchmark functions of 30 and 200 decision variables having different properties. The experiments study the effects of its new probability parameter Px and swarm size for optimizing those functions. The results are analyzed and compared with those from the Standard PSO [14] and G3PCX algorithms. The proposed PSPG with Px = 0.10 and 0.15 can outperform both algorithms with a statistical significance for most functions. In addition, the PSPG is not much sensitive to its swarm size as most PSO algorithms are. The best swarm sizes are 40 and 50 for unimodal and multimodal functions, respectively, of 30 decision variables.

The need of cloud consumers to optimize all options offered by cloud provider has been rapidly arisen during the recent years. The consideration involves the appropriate number of VMs must be purchased along with the allocation of supporting resources. Moreover, commercial clouds may have many different purchasing options. Finding optimal provisioning solutions is thus an NP-hard problem. Currently, there are many research works discussing the cloud provisioning cost optimization. However, most of the works mainly concerned with task scheduling. In this paper, we proposed a new framework where number of purchased instance, instance type, purchasing options, and task scheduling are considered within an optimization process. The Particle Swarm Optimization (PSO) technique is used to find the optimal solution. The initial results show a promising performance in both the perspectives of the total cost and fitness convergence. The designed system provides the solutions of purchasing options with optimum budget for any specified workflow-based application based on the required performance.

In 1995 swarm intelligence based PSO (Particle Swarm Optimization) has been designed and implemented for solving optimization problems. Since then many researchers have developed many versions, based upon its theoretical concept, technical aspects and parameters involve in the algorithm. In broad sense, every swarm updates its position based upon the knowledge of its initial velocity and accelerating components such as cognitive and social information. In this paper we have presented a reformed and modified concept of PSO with the thought that every swarm updates its position based upon cognitive and social environment knowledge only and the key aspect used here is that these parameters are no longer assumed to be accelerating components rather position components. This algorithm is termed by us as Cognitive and Social Information based PSO (CSIPSO). The performance of CSI-PSO is validated by 23 benchmark functions and the empirical results clearly support the effectiveness of our concept.Keywords: Particle Swarm, PSO, Swarm Theory, Benchmark functions

In an electricity market generating companies and large consumers need suitable bidding models to maximize their profits. Therefore, each supplier and large consumer will bid strategically for choosing the bidding coefficients to counter the competitors bidding strategy. In this paper, bidding strategy problem modeled as an optimization problem and solved using Particle Swarm Optimization (PSO). PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithm (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. A numerical example with six suppliers and two large consumers is used to illustrate the essential features of the proposed method and the results are compared with the Genetic Algorithm (GA) approach. Test results indicate that the proposed algorithm outperforms the Genetic Algorithm approach with respect to total profit and convergence time.

GUVENC, Ugur/0000-0002-5193-7990; Duman, Serhat/0000-0002-1091-125X WOS: 000288051500024 U. Guvenc, S. Duman, B. Saracoglu, A. Ozturk. A Hybrid GA-PSO Approach Based on Similarity for Various Types of Economic Dispatch Problems // Electronics and Electrical Engineering. - Kaunas: Technologija, 2011. - No. 2(108). P. 109-114. Economic dispatch problem is an optimization problem where objective function is highly nonlinear. In this paper, an efficient method based on hybrid genetic algorithm- particle swarm optimization (GA-PSO) for economic dispatch (ED) problem is proposed. In the proposed method, children created by using similarity measurement between mother and father chromosomes relationship. The feasibility of the proposed approach is demonstrated for solve various types of economic dispatch (ED) problems in power systems such as, economic dispatch with valve point (EDVP) effects, the ED of generators with prohibited operating zones and ED with only fuel options and it is compared in the recent literature. The study results show that the proposed approach is more efficient in finding higher quality solutions in various type ED problems. Ill. 3, bibl. 20, tabl. 10 (in English; abstracts in English and Lithuanian).

This paper deals with a design of optimal parameters of PI regulator in voltage control loop. The design aims to minimize voltampere (VA) loading of synchronous reference frame (SRF) based multi converter-DVR in parallel feeder distribution systems. To improve the power quality by using power conditioner with multi converters in each feeder distribution system under different load considerations, a dynamic model of synchronous-reference- frame (SRF) has been adopted. It is desirable to have optimization of PI components and generate the reference compensate voltages for series active filters (SAF) of MC-DVR. Therefore, series active filters help to inject optimal voltages at optimal angle in such a way that a minimum VA loading of MCDVR is possible. The optimal value PI components and optimal series injected angle of series active power filters are computed using particle swarm optimization (PSO) technique and the results are verified with genetic algorithm (GA). The simulation results show that the proposed PSO technique causes minimum error and minimum VA loading angle by optimally choosing parameters of PI and minimum VA angle as compared to GA technique.Keywords: Active power filter (APF), synchronous reference frame (SRF), Genetic algorithm (GA), Particle Swarm Optimization (PSO) and multi converter dynamic voltage restorer (MC-DVR).

Visual attention mechanism is one of the important techniques in computer vision field, and it can increase the effectiveness of computer image information processing. Biogeography-Based Optimization (BBO) is a bioinspired algorithm for global optimization, searching for the global optimum mainly through two steps: migration and mutation. In this paper, a novel Predator-Prey Biogeography-Based Optimization (PPBBO) is utilized to solve the bio-inspired visual attention problem. In PPBBO method, BBO is combined with the mechanism of predatorprey, which can enhance the global convergence of the algorithm. The convergence property of the PPBBO is analyzed theoretically, and the detailed process is also given. Comparative experimental results with basic BBO, CBBO, CPPBBO, and Particle Swarm Optimization (PSO) demonstrate the feasibility and effectiveness of our presented PPBBO for adjusting combination of feature maps in visual attention.