A Fast and Stable Method for a Simplified Short-Term Hydrothermal Scheduling Problem using Particle Swarm Optimization and Direct Search

2013 ◽  
Vol 2 (4) ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Direct Search ◽  
Scheduling Problem ◽  
Stable Method ◽  
Short Term ◽  
Swarm Optimization ◽  
Hydrothermal Scheduling

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.

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