Discrete Version Of Particle Swarm Optimization Research Articles

Parameter Selection of Discrete Particle Swarm Optimization Algorithm for the Quadratic Assignment Problems

Quadratic Assignment Problems (QAPs) are the hardest of combinatorial optimization problems, with some problems of sizes of the order of 30 still remaining unsolved optimally. Solving these problems with exact optimization methods is cumbersome and hence the use of non-traditional optimization methods is advisable. The discrete version of Particle Swarm Optimization (DPSO) is a widely accepted non-conventional optimization algorithm for combinatorial optimization problems. This paper uses Taguchi's robust design approach to determine the best combination of parameters for discrete particle swarm optimization algorithm for solving quadratic assignment problems.

Discrete Multi Objective Particle Swarm Optimization Algorithm for FPGA Placement

Placement process is one of the vital stages in physical design. In this stage, modules and elements of the circuit are placed in distinct locations based on optimizationp rocesses. Hence, each placement process influences one or more optimization factor. On the other hand, it can be statedunequiv ocally that FPGA is one of the most important and applicable devices in our electronic world. So, it is vital to spend time forbetter learning of its structure. VLSI science looks for new techniques for minimizing the expense of FPGA in order to gain better performance. Diverse algorithms are used for running FPGA placement procedures. It is known that particle swarm optimization (PSO) is one of the practical evolutionary algorithms for this kind of applications. So, this algorithm is used for solving placement problems. In this work, a novel method for optimized FPGA placement has been used. According to this process, the goal is to optimize two objectives defined as wire length and overlap removal functions. Consequently, we are forced to use multi-objective particle swarm optimization (MOPSO) in the algorithm. Structure of MOPSO is such that it introduces set of answers among which we have tried to find a unique answer with minimum overlap. Itis worth noting that discrete nature of FPGA blocks forced us to use a discrete version of PSO. In fact, we need a combination of multi-objecti ve PSO and discrete PSO for achieving our goals in optimization process. Tested on some of FPGA benchmark (MCNC benchmark) are shown in experimental results section, compared with popular method VPR. These show that proper selection of FPGA 's size and reasonable number of blocks can giveus good response.

Simple Particle Swarm Optimization for Solving Beam-Slab Layout Design Problems

This study presents a particle swarm optimization (PSO) algorithm for beam-slab layout design. Design of beam-slab layouts is considered a heuristic task rather than an algorithmic task since its process cannot be written as an algorithm. In this study, this design task is expressed as an optimization problem. This is done by establishing appropriate objective and constraint functions based on selected suitable engineering consideration. The obtained optimization problem is then solved by a simple PSO algorithm. PSO is a rather new optimization method that is inspired by the flocking behavior of fishes and birds. Recently, it has gained popularity because its algorithm is simple and there has been evidence of its excellent performance. To employ PSO in solving the optimization problem that represents the beam-slab layout design problem, a suitable coding scheme for beam-slab layouts is used. Since the beam-slab layout design problem deals with a discrete search space of beam-slab layouts, a discrete version of PSO is employed in this study. The proposed PSO algorithm is tested by solving the beam-slab layout design problem of a rectangular floor. The validity of the proposed algorithm is observed from the obtained results.

Optimal Capacitor Placement and Sizing in Unbalanced Distribution Systems With Harmonics Consideration Using Particle Swarm Optimization

Shunt capacitors installation in distribution systems requires optimal placement and sizing. More harmonics are being injected into distribution systems. Adding shunt capacitors may lead to high distortion levels. The capacitor placement and sizing problem is a nonlinear integer optimization problem, with locations and ratings of shunt capacitors being discrete values. The goal is to minimize the overall cost of the total real power loss and that of shunt capacitors while satisfying operating and power quality constraints. This paper proposes to solve the problem using particle swarm optimization (PSO). A discrete version of PSO is combined with a radial distribution power flow algorithm (RDPF) to form a hybrid PSO algorithm (HPSO). The former is employed as a global optimizer to find the global optimal solution, while the latter is used to calculate the objective function and to verify bus voltage limits. To include the presence of harmonics, the developed HPSO was integrated with a harmonic power flow algorithm (HPF). The proposed (HPSO-HPF)-based approach is tested on an IEEE 13-bus radial distribution system (13-Bus-RDS). The findings clearly demonstrate the necessity of including harmonics in optimal capacitor placement and sizing to avoid any possible problems associated with harmonics.

A diversity-enriched variant of discrete PSO applied to the design of water distribution networks

The design of water distribution networks (WDNs) is addressed by using a variant of the particle swarm optimization (PSO) algorithm. This variant, which makes use of a discrete version of PSO already considered by the authors, overcomes one of the PSO's main drawbacks, namely its difficulty in maintaining acceptable levels of population diversity and in balancing local and global searches. The performance of the variant proposed here is investigated by applying the model to solve two standard benchmark problems: the Hanoi new water distribution network and the New York Tunnel water supply system. The results obtained show considerable improvements in both convergence characteristics and the quality of the final solutions, and near-optimal results are consistently achieved at reduced computational cost.

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.