Refined Heuristic Swarm Intelligence Algorithm

Paintball has gained a huge popularity in Malaysia with growing number of tournaments organized nationwide. Currently, Ideal Pro Event, one of the paintball organizer found difficulties to pair a suitable opponent to against one another in a tournament. This is largely due to the manual matchmaking method that only randomly matches one team with another. Consequently, it is crucial to ensure a balanced tournament bracket where eventual winners and losers not facing one another in the very first round. This study proposes an intelligent matchmaking using Particle Swarm Optimization (PSO) and tournament management system for paintball organizers. PSO is a swarm intelligence algorithm that optimizes problems by gradually improving its current solutions, therefore countenancing the tournament bracket to be continually improved until the best is produced. Indirectly, through the development of the system, it is consider as an intelligence business idea since it able to save time and enhance the company productivity. This algorithm has been tested using 3 size of population; 100, 1000 and 10,000. As a result, the speed of convergence is consistent and has not been affected through big population.

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REAL POWER LOSS REDUCTION & VOLTAGE STABILITY AMPLIFICATION BY HYBRIDIZATION OF RESTARTED SIMULATED ANNEALING WITH PARTICLE SWARM OPTIMIZATION ALGORITHM

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v6.i9.2018.1229 ◽

2018 ◽

Vol 6 (9) ◽

pp. 246-258

Author(s):

K. Lenin

Keyword(s):

Particle Swarm Optimization ◽

Simulated Annealing ◽

Swarm Intelligence ◽

Optimization Algorithm ◽

Voltage Stability ◽

Reactive Power ◽

Particle Swarm ◽

Swarm Optimization ◽

Intelligence Algorithm ◽

Swarm Intelligence Algorithm

This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. The Simulated Annealing (SA) algorithm is a probabilistic hill-climbing technique that is based on the annealing/cooling process of metals. In total, most moves may be accepted at initial stages, but at the final stage only improving ones are likely to be allowed. This can help the procedure jump out of a local minimum. However, sometimes it is better to move back to a former solution that was significantly better rather than always moving from the current state. This process is called “restarting” of SA & called as Restarted Simulated Annealing (RSA). In this paper we proposed a hybridized restarted simulated annealing particle swarm optimization (RSAPSO) technique to find global minima more efficiently and robustly. The proposed RSAPSO combines the global search ability of PSO and the local search ability of RSA, and offsets the weaknesses of each other. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other reported algorithms.

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An Improved Particle Swarm Optimization Algorithm with Adaptive Inertia Weights

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622019500147 ◽

2019 ◽

Vol 18 (03) ◽

pp. 833-866 ◽

Cited By ~ 8

Author(s):

Mi Li ◽

Huan Chen ◽

Xiaodong Wang ◽

Ning Zhong ◽

Shengfu Lu

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Pso Algorithm ◽

Local Optimum ◽

Random Mutation ◽

Swarm Optimization ◽

Inertia Weight ◽

Fitness Value ◽

The One ◽

Individual Particles

The particle swarm optimization (PSO) algorithm is simple to implement and converges quickly, but it easily falls into a local optimum; on the one hand, it lacks the ability to balance global exploration and local exploitation of the population, and on the other hand, the population lacks diversity. To solve these problems, this paper proposes an improved adaptive inertia weight particle swarm optimization (AIWPSO) algorithm. The AIWPSO algorithm includes two strategies: (1) An inertia weight adjustment method based on the optimal fitness value of individual particles is proposed, so that different particles have different inertia weights. This method increases the diversity of inertia weights and is conducive to balancing the capabilities of global exploration and local exploitation. (2) A mutation threshold is used to determine which particles need to be mutated. This method compensates for the inaccuracy of random mutation, effectively increasing the diversity of the population. To evaluate the performance of the proposed AIWPSO algorithm, benchmark functions are used for testing. The results show that AIWPSO achieves satisfactory results compared with those of other PSO algorithms. This outcome shows that the AIWPSO algorithm is conducive to balancing the abilities of the global exploration and local exploitation of the population, while increasing the diversity of the population, thereby significantly improving the optimization ability of the PSO algorithm.

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On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

The Scientific World JOURNAL ◽

10.1155/2013/860289 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 30

Author(s):

Martins Akugbe Arasomwan ◽

Aderemi Oluyinka Adewumi

Keyword(s):

Global Optimization ◽

Particle Swarm Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Pso Algorithm ◽

Search Space ◽

Terminal Point ◽

Simulation Experiments ◽

Swarm Optimization ◽

Inertia Weight

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted.

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Particle Swarm Optimization with Team Spirit Inertia Weight

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.383-390.5744 ◽

2011 ◽

Vol 383-390 ◽

pp. 5744-5750

Author(s):

Xi Zhen Wang ◽

Yan Li ◽

Gang Hu Cheng

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Pso Algorithm ◽

Convergence Problem ◽

Swarm Optimization ◽

Inertia Weight ◽

Standard Particle Swarm Optimization ◽

Decline Curves ◽

Team Spirit ◽

Global And Local

A PSO Algorithm with Team Spirit Inertia weight (TSWPSO) is presented based on the study of the effect of inertia weight on Standard Particle Swarm Optimization (SPSO). Due to the theory of group in organization psychology, swarm is divided into multiple sub-swarms and search is run in a number of different sub-swarms which are parallel performed. Try to find or modify a curve which is compatible with optimized object within many inertia weight decline curves, in order to balance the global and local explorations ability in particle swarm optimization and to avoid the premature convergence problem effectively. The testes by five classical functions show that, TSWPSO has a better performance in both the convergence rate and the precision.

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A Particle Swarm Optimization with Variety Factor

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.239-240.1291 ◽

2012 ◽

Vol 239-240 ◽

pp. 1291-1297 ◽

Cited By ~ 1

Author(s):

Hai Sheng Qin ◽

Deng Yue Wei ◽

Jun Hui Li ◽

Lei Zhang ◽

Yan Qiang Feng

Keyword(s):

Particle Swarm Optimization ◽

Convergence Property ◽

Particle Swarm ◽

Pso Algorithm ◽

Premature Convergence ◽

Simulation Experiments ◽

Swarm Optimization ◽

Inertia Weight ◽

Previous Algorithm ◽

Modified Pso

A new particle swarm optimization (PSO) algorithm (a PSO with Variety Factor, VFPSO) based on the PSO was proposed. Compared with the previous algorithm, the proposed algorithm is to update the Variety Factor and to improve the inertia weight of the PSO. The target of the improvement is that the new algorithm could go on enhancing the robustness as before and should reduce the risk of premature convergence. The simulation experiments show that it has great advantages of convergence property over some other modified PSO algorithms, and also avoids algorithm being trapped in local minimum effectively. So it can avoid the phenomenon of premature convergence.

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Resolving the optimal selection of a natural reserve using the particle swarm optimisation by applying transfer functions

10.7287/peerj.preprints.26941 ◽

2018 ◽

Author(s):

Boris Almonacid

Keyword(s):

Particle Swarm Optimization ◽

Transfer Functions ◽

Particle Swarm ◽

Pso Algorithm ◽

Optimal Selection ◽

Metaheuristic Algorithm ◽

Swarm Optimization ◽

Natural Reserve ◽

Transfer Method ◽

Selection Of

The optimal selection of a natural reserve (OSRN) is an optimisation problem with a binary domain. To solve this problem the metaheuristic algorithm Particle Swarm Optimization (PSO) has been chosen. The PSO algorithm has been designed to solve problems in real domains. Therefore, a transfer method has been applied that converts the equations with real domains of the PSO algorithm into binary results that are compatible with the OSRN problem. Four transfer functions have been tested in four case studies to solve the OSRN problem. According to the tests carried out, it is concluded that two of the four transfer functions are apt to solve the problem of optimal selection of a natural reserve.

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Resolving the optimal selection of a natural reserve using the particle swarm optimisation by applying transfer functions

10.7287/peerj.preprints.26941v1 ◽

2018 ◽

Author(s):

Boris L Almonacid

Keyword(s):

Particle Swarm Optimization ◽

Transfer Functions ◽

Particle Swarm ◽

Pso Algorithm ◽

Optimal Selection ◽

Metaheuristic Algorithm ◽

Swarm Optimization ◽

Natural Reserve ◽

Transfer Method ◽

Selection Of

The optimal selection of a natural reserve (OSRN) is an optimisation problem with a binary domain. To solve this problem the metaheuristic algorithm Particle Swarm Optimization (PSO) has been chosen. The PSO algorithm has been designed to solve problems in real domains. Therefore, a transfer method has been applied that converts the equations with real domains of the PSO algorithm into binary results that are compatible with the OSRN problem. Four transfer functions have been tested in four case studies to solve the OSRN problem. According to the tests carried out, it is concluded that two of the four transfer functions are apt to solve the problem of optimal selection of a natural reserve.

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Enriched Particle Swarm Optimization Created By Varying Parameters for Optimization

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6574.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 8259-8265

Keyword(s):

Particle Swarm Optimization ◽

Swarm Intelligence ◽

Optimization Problems ◽

Real Life ◽

Particle Swarm ◽

Swarm Optimization ◽

Slow Convergence ◽

Inertia Weight ◽

Life Problems ◽

Fixed Constant

Particle swarm optimization (PSO) is one of the most capable algorithms that reside to the swarm intelligence (SI) systems. Recently, it becomes very popular and renowned because of the easy implementation in complex/real life optimization problems. However, PSO has some observable drawbacks such as diversity maintenance, pre convergence and/or slow convergence speed. The ultimate success of PSO depends on the velocity update of the particles. Velocity has a significant dependence on its multiplied coefficient like inertia weight and acceleration factors. To increase the ability of PSO, this paper introduced an enriched PSO (namely ePSO), to solve hard optimization problems more precisely, efficiently and reliably. In ePSO novel gradually decreased inertia weight (as an alternative of a fixed constant value) and new gradually decreased and/or increased acceleration factors (meant for cognitive and social modules) is introduced. Proposed ePSO is used to solve four well known typical unconstrained benchmark functions and four complex unconstrained real life problems. The overall observation shows that proposed new algorithm ePSO is fitter than the compared algorithms significantly and statistically. Moreover, the convergence accuracy and speed of ePSO are also improved effectively

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