scholarly journals Statistical Performances Evaluation of APSO and Improved APSO for Short Term Hydrothermal Scheduling Problem

2019 ◽  
Vol 9 (12) ◽  
pp. 2440 ◽  
Author(s):  
Muhammad Salman Fakhar ◽  
Syed Abdul Rahman Kashif ◽  
Noor Ul Ain ◽  
Hafiz Zaheer Hussain ◽  
Akhtar Rasool ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Scheduling Problem ◽  
Short Term ◽  
Swarm Optimization ◽  
Hydrothermal Scheduling ◽  
Dynamic Search ◽  
Accelerated Particle Swarm Optimization

The Accelerated Particle Swarm Optimization (APSO) algorithm is an efficient and the easiest to implement variant of the famous Particle Swarm Optimization (PSO) algorithm. PSO and its variant APSO have been implemented on the famous Short-Term Hydrothermal Scheduling (STHTS) problem in recent research, and they have shown promising results. The APSO algorithm can be further modified to enhance its optimizing capability by deploying dynamic search space squeezing. This paper presents the implementation of the improved APSO algorithm that is based on dynamic search space squeezing, on the short-term hydrothermal scheduling problem. To give a quantitative comparison, a true statistical comparison based on comparing means is also presented to draw conclusions.

scholarly journals A Chaotic Quantum Behaved Particle Swarm Optimization Algorithm for Short-term Hydrothermal Scheduling

2017 ◽  
Vol 11 (1) ◽  
pp. 23-37 ◽  
Author(s):  
Chen Gonggui ◽  
Huang Shanwai ◽  
Sun Zhi
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Particle Swarm ◽  
Inequality Constraints ◽  
Population Based ◽  
Scheduling Problem ◽  
Short Term ◽  
Swarm Optimization ◽  
Hydrothermal Scheduling ◽  
Comparison Results

This study proposes a novel chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm for solving short-term hydrothermal scheduling problem with a set of equality and inequality constraints. In the proposed method, chaotic local search technique is employed to enhance the local search capability and convergence rate of the algorithm. In addition, a novel constraint handling strategy is presented to deal with the complicated equality constrains and then ensures the feasibility and effectiveness of solution. A system including four hydro plants coupled hydraulically and three thermal plants has been tested by the proposed algorithm. The results are compared with particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) and other population-based artificial intelligence algorithms considered. Comparison results reveal that the proposed method can cope with short-term hydrothermal scheduling problem and outperforms other evolutionary methods in the literature.

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

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
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.

An Improved Particle Swarm Optimization Algorithm for Global Multidimensional Optimization

2017 ◽  
Vol 29 (1) ◽  
pp. 127-142
Author(s):  
Rkia Fajr ◽  
Abdelaziz Bouroumi
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Strategies ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Human Learning ◽  
Computing Methods ◽  
Swarm Optimization ◽  
Starting Point ◽  
New Variant

Abstract This paper introduces a new variant of the particle swarm optimization (PSO) algorithm, designed for global optimization of multidimensional functions. The goal of this variant, called ImPSO, is to improve the exploration and exploitation abilities of the algorithm by introducing a new operation in the iterative search process. The use of this operation is governed by a stochastic rule that ensures either the exploration of new regions of the search space or the exploitation of good intermediate solutions. The proposed method is inspired by collaborative human learning and uses as a starting point a basic PSO variant with constriction factor and velocity clamping. Simulation results that show the ability of ImPSO to locate the global optima of multidimensional functions are presented for 10 well-know benchmark functions from CEC-2013 and CEC-2005. These results are compared with the PSO variant used as starting point, three other PSO variants, one of which is based on human learning strategies, and three alternative evolutionary computing methods.

scholarly journals Particle Swarm Optimization

2017 ◽  
pp. 614-635 ◽  
Author(s):  
Alaa Tharwat ◽  
Tarek Gaber ◽  
Aboul Ella Hassanien ◽  
Basem E. Elnaghi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Parameter Tuning ◽  
Pso Algorithm ◽  
Search Space ◽  
Learning Problems ◽  
Swarm Optimization ◽  
Numerical Example

Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm Optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper highlights the basic background needed to understand and implement the PSO algorithm. This paper starts with basic definitions of the PSO algorithm and how the particles are moved in the search space to find the optimal or near optimal solution. Moreover, a numerical example is illustrated to show how the particles are moved in a convex optimization problem. Another numerical example is illustrated to show how the PSO trapped in a local minima problem. Two experiments are conducted to show how the PSO searches for the optimal parameters in one-dimensional and two-dimensional spaces to solve machine learning problems.

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