A General Intelligent Optimization Algorithm Combination Framework with Application in Economic Load Dispatch Problems

Jinghua Zhang,Ze Dong

doi:10.3390/en12112175

Abstract

Recently, a population-based intelligent optimization algorithm research has been combined with multiple algorithms or algorithm components in order to improve the performance and robustness of an optimization algorithm. This paper introduces the idea into real world application. Different from traditional algorithm research, this paper implements this idea as a general framework. The combination of multiple algorithms or algorithm components is regarded as a complex multi-behavior population, and a unified multi-behavior combination model is proposed. A general agent-based algorithm framework is designed to support the model, and various multi-behavior combination algorithms can be customized under the framework. Then, the paper customizes a multi-behavior combination algorithm and applies the algorithm to solve the economic load dispatch problems. The algorithm has been tested with four test systems. The test results prove that the multi-behavior combination idea is meaningful which also indicates the significance of the framework.

Highlights

Many real-world problems can be modeled as optimization tasks
Considering the population performing the optimization as a stochastic system, information entropy can be used to evaluate the state of the population
The information entropy for discrete information is shown as follows: n i=1 where n represents the number of discrete random variables, and pi represents the probability of the ith discrete random variable, pi ∈ [0,1], and p1 + p2 +...+ pn = 1

Summary

Introduction

Many real-world problems can be modeled as optimization tasks. It is an important solution to solve the optimization problem with the population-based intelligent optimization algorithm such as evolutionary computation, swarm intelligence.In the early stage, population-based intelligent optimization algorithms tend to be concise, and population intelligence is acquired through the simple behavior of individuals. Many real-world problems can be modeled as optimization tasks. It is an important solution to solve the optimization problem with the population-based intelligent optimization algorithm such as evolutionary computation, swarm intelligence. Some of the optimization problems in the real world are more complicated, and the existing algorithms may reflect their shortcomings. Economic load dispatch (ELD) is a typical optimization problem in power systems. The goal of ELD is to rationally arrange the power output of each generating unit in a power plant or power system so as to minimize fuel costs on the premise of system load and operational constraints. As the scale of the power system increases and the model for the problem becomes more sophisticated by considering more conditions, the optimization task becomes more difficult. Population-based intelligent optimization algorithms have been applied to solve ELD problems. There are some studies that improve the classical algorithm for the ELD problem, Energies 2019, 12, 2175; doi:10.3390/en12112175 www.mdpi.com/journal/energies

Methods

Results

Discussion

Conclusion