Multi Echelon Supply Chain Optimization Using Particle Swarm Intelligence Algorithm

2007 ◽  
Vol 8 (2-4) ◽  
pp. 199-212
Author(s):  
R. Kadadevaramath, ◽  
K.M. Mohanasundaram, ◽  
K. Rameshkumar, ◽  
B. Chandrashekhar,
Keyword(s):  
Supply Chain ◽  
Swarm Intelligence ◽  
Particle Swarm ◽  
Supply Chain Optimization ◽  
Particle Swarm Intelligence ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

scholarly journals Refined Heuristic Swarm Intelligence Algorithm

2020 ◽  
pp. 267-275
Author(s):  
I. I. Aina ◽  
C. N. Ejieji
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

In this paper, a new metaheuristic algorithm named refined heuristic intelligence swarm (RHIS) algorithm is developed from an existing particle swarm optimization (PSO) algorithm by introducing a disturbing term to the velocity of PSO and modifying the inertia weight, in which the comparison between the two algorithms is also addressed.

scholarly journals Business Intelligence for Paintball Tournament Matchmaking Using Particle Swarm Optimization

2018 ◽  
Vol 11 (2) ◽  
pp. 599
Author(s):  
M.T. Mishan ◽  
A.F.A. Fadzil ◽  
K.A.F.A. Samah ◽  
N.F. Baharin ◽  
N. Anuar
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Business Intelligence ◽  
Management System ◽  
Particle Swarm ◽  
Speed Of Convergence ◽  
Swarm Optimization ◽  
Intelligence Algorithm ◽  
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.

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

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.

scholarly journals Optimization of a Five-Echelon Supply Chain Network using Particle Swarm Intelligence Algorithms

2019 ◽  
Vol 8 (9) ◽  
pp. 2015-2022
Keyword(s):  
Supply Chain ◽  
Swarm Intelligence ◽  
Production Systems ◽  
Particle Swarm ◽  
Transportation Cost ◽  
Chain Model ◽  
Business Organizations ◽  
Particle Swarm Intelligence ◽  
Supply Chain Model ◽  
The Right

Technology has shrunk the global markets and information is accessible very quickly and effortlessly. Business organizations world over concentrate on their production systems to improve the quality of the end product, well distribute the product and optimize cost of resources. Transportation cost, inventory carrying cost and shortage costs constitute the major costs in cost of distribution. A competent supply chain always strives to manufacture the right quantity of end products and hold a minimum inventory across the entire supply chain. In thecurrent paper, a five echelon supply chain model is developed and it is optimized using particle swarm intelligence algorithm.

Application of particle swarm intelligence algorithms in supply chain network architecture optimization

2012 ◽  
Vol 39 (11) ◽  
pp. 10160-10176 ◽  
Author(s):  
Rajeshwar S. Kadadevaramath ◽  
Jason C.H. Chen ◽  
B. Latha Shankar ◽  
K. Rameshkumar
Keyword(s):  
Supply Chain ◽  
Swarm Intelligence ◽  
Network Architecture ◽  
Particle Swarm ◽  
Supply Chain Network ◽  
Particle Swarm Intelligence ◽  
Architecture Optimization
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