A synergy of binary differential evolution and binary local search optimizer to solve multi-objective profit based unit commitment problem

2021 ◽  
Vol 107 ◽  
pp. 107387
Author(s):  
Jatinder Singh Dhaliwal ◽  
J.S. Dhillon
Keyword(s):  
Local Search ◽  
Differential Evolution ◽  
Unit Commitment ◽  
Unit Commitment Problem ◽  
Multi Objective ◽  
Commitment Problem ◽  
Binary Differential Evolution

Scope of Biogeography Based Optimization for Economic Load Dispatch and Multi-Objective Unit Commitment Problem

2014 ◽  
Vol 3 (4) ◽  
pp. 34-54 ◽  
Author(s):  
Vikram Kumar Kamboj ◽  
S.K. Bath
Keyword(s):  
Optimization Algorithm ◽  
Unit Commitment ◽  
Optimization Problems ◽  
Simple Procedure ◽  
Mathematical Formulation ◽  
Economic Load Dispatch ◽  
Unit Commitment Problem ◽  
Multi Objective ◽  
Commitment Problem ◽  
Population Based Algorithm

Biogeography Based Optimization (BBO) algorithm is a population-based algorithm based on biogeography concept, which uses the idea of the migration strategy of animals or other spices for solving optimization problems. Biogeography Based Optimization algorithm has a simple procedure to find the optimal solution for the non-smooth and non-convex problems through the steps of migration and mutation. This research paper presents the solution to Economic Load Dispatch Problem for IEEE 3, 4, 6 and 10-unit generating model using Biogeography Based Optimization algorithm. It also presents the mathematical formulation of scalar and multi-objective unit commitment problem, which is a further extension of economic load dispatch problem.

scholarly journals Eagle Strategy based Crow Search Algorithm for solving Unit Commitment Problem

2018 ◽  
Vol 12 (1) ◽  
pp. 17 ◽  
Author(s):  
Rachid Habachi ◽  
Achraf Touil ◽  
Abdelkabir Charkaoui ◽  
Abdelwahed Echchatbi
Keyword(s):  
Local Search ◽  
Unit Commitment ◽  
Search Algorithm ◽  
Optimization Strategy ◽  
Unit Commitment Problem ◽  
Spinning Reserve ◽  
Two Stage ◽  
Commitment Problem ◽  
Local Optimizer ◽  
Eagle Strategy

<p>Eagle strategy is a two-stage optimization strategy, which is inspired by the observation of the hunting behavior of eagles in nature. In this two-stage strategy, the first stage explores the search space globally by using a Levy flight; if it finds a promising solution, then an intensive local search is employed using a more efficient local optimizer, such as hillclimbing and the downhill simplex method. Then, the two-stage process starts again with new global exploration, followed by a local search in a new region. One of the remarkable advantages of such a combina-tion is to use a balanced tradeoff between global search (which is generally slow) and a rapid local search. The crow search algorithm (CSA) is a recently developed metaheuristic search algorithm inspired by the intelligent behavior of crows.This research article integrates the crow search algorithm as a local optimizer of Eagle strategy to solve unit commitment (UC) problem. The Unit commitment problem (UCP) is mainly finding the minimum cost schedule to a set of generators by turning each one either on or off over a given time horizon to meet the demand load and satisfy different operational constraints. There are many constraints in unit commitment problem such as spinning reserve, minimum up/down, crew, must run and fuel constraints. The proposed strategy ES-CSA is tested on 10 to 100 unit systems with a 24-h scheduling horizon. The effectiveness of the proposed strategy is compared with other well-known evolutionary, heuristics and meta-heuristics search algorithms, and by reported numerical results, it has been found that proposed strategy yields global results for the solution of the unit commitment problem.</p><p> </p>

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