scholarly journals A Novel Tournament Selection Based Differential Evolution Variant for Continuous Optimization Problems

2015 ◽  
Vol 2015 ◽  
pp. 1-21 ◽  
Author(s):  
Qamar Abbas ◽  
Jamil Ahmad ◽  
Hajira Jabeen
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Convergence Speed ◽  
Local Optima ◽  
De Algorithm ◽  
Parent Selection ◽  
Tournament Selection ◽  
Overall Performance ◽  
Continuous Optimization Problems

Differential evolution (DE) is a powerful global optimization algorithm which has been studied intensively by many researchers in the recent years. A number of variants have been established for the algorithm that makes DE more applicable. However, most of the variants are suffering from the problems of convergence speed and local optima. A novel tournament based parent selection variant of DE algorithm is proposed in this research. The proposed variant enhances searching capability and improves convergence speed of DE algorithm. This paper also presents a novel statistical comparison of existing DE mutation variants which categorizes these variants in terms of their overall performance. Experimental results show that the proposed DE variant has significance performance over other DE mutation variants.

scholarly journals Hydrological Cycle Algorithm for Continuous Optimization Problems

2017 ◽  
Vol 2017 ◽  
pp. 1-25 ◽  
Author(s):  
Ahmad Wedyan ◽  
Jacqueline Whalley ◽  
Ajit Narayanan
Keyword(s):  
Optimization Problems ◽  
Hydrological Cycle ◽  
Water Cycle ◽  
Continuous Optimization ◽  
Global Solutions ◽  
Solution Quality ◽  
Local Optima ◽  
Water Based ◽  
Water Drops ◽  
Continuous Optimization Problems

A new nature-inspired optimization algorithm called the Hydrological Cycle Algorithm (HCA) is proposed based on the continuous movement of water in nature. In the HCA, a collection of water drops passes through various hydrological water cycle stages, such as flow, evaporation, condensation, and precipitation. Each stage plays an important role in generating solutions and avoiding premature convergence. The HCA shares information by direct and indirect communication among the water drops, which improves solution quality. Similarities and differences between HCA and other water-based algorithms are identified, and the implications of these differences on overall performance are discussed. A new topological representation for problems with a continuous domain is proposed. In proof-of-concept experiments, the HCA is applied on a variety of benchmarked continuous numerical functions. The results were found to be competitive in comparison to a number of other algorithms and validate the effectiveness of HCA. Also demonstrated is the ability of HCA to escape from local optima solutions and converge to global solutions. Thus, HCA provides an alternative approach to tackling various types of multimodal continuous optimization problems as well as an overall framework for water-based particle algorithms in general.

An Improved Differential Evolution Algorithm for Numerical Optimization Problems

2013 ◽  
Vol 415 ◽  
pp. 349-352
Author(s):  
Hong Wei Zhao ◽  
Hong Gang Xia
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Local Optimum ◽  
Local Optima ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

Differential evolution (DE) is a population-based stochastic function minimizer (or maximizer), whose simple yet powerful and straightforward features make it very attractive for numerical optimization. However, DE is easy to trapped into local optima. In this paper, an improved differential evolution algorithm (IDE) proposed to speed the convergence rate of DE and enhance the global search of DE. The IDE employed a new mutation operation and modified crossover operation. The former can rapidly enhance the convergence of the MDE, and the latter can prevent the MDE from being trapped into the local optimum effectively. Besides, we dynamic adjust the scaling factor (F) and the crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the IDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and other algorithms (PSO and JADE) that reported in recent literature.

scholarly journals Enhancing Differential Evolution on Continuous Optimization Problems by Detecting Promising Leaders

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 226557-226578
Author(s):  
Yiqiao Cai ◽  
Duanwei Wu ◽  
Shaopeng Liu ◽  
Shunkai Fu ◽  
Peizhong Liu
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Continuous Optimization Problems

scholarly journals An Adaptive Differential Evolution Algorithm with Restart for Solving Continuous Optimization Problems

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Continuous Optimization ◽  
General Purpose ◽  
Adaptive Parameter ◽  
Adaptive Differential Evolution ◽  
Use Efficiency ◽  
Evolution Algorithm ◽  
Continuous Optimization Problems

A new adaptive differential evolution algorithm with restart (ADE-R) is proposed as a general-purpose method for solving continuous optimization problems. Its design aims at simplicity of use, efficiency and robustness. ADE-R simulates a population evolution of real vectors using vector mixing operations with an adaptive parameter control based on the switching of two selected intervals of values for each scaling factor and crossover rate of the basic differential evolution algorithm. It also incorporates a restart technique to supply new contents to the population to prevent premature convergence and stagnation. The method is tested on several benchmark functions covering various types of functions and compared with some well-known and state-of-art methods. The experimental results show that ADE-R is effective and outperforms the compared methods.

An Efficient Hybrid Algorithm Based on HS and SFLA

2016 ◽  
Vol 30 (05) ◽  
pp. 1659012 ◽  
Author(s):  
Aijia Ouyang ◽  
Xuyu Peng ◽  
Yanbin Liu ◽  
Lilue Fan ◽  
Kenli Li
Keyword(s):  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Harmony Search ◽  
Complex Function ◽  
Continuous Optimization ◽  
Function Optimization ◽  
Local Optima ◽  
Hybrid Mechanism ◽  
Complex Functions ◽  
Continuous Optimization Problems

When used for optimizing complex functions, harmony search (HS) and shuffled frog leaping algorithm (SFLA) algorithm tend to easily get trapped into local optima and result in low convergence precision. To overcome such shortcomings, a hybrid mechanism of selective search by combining HS algorithm and SFLA algorithm is as well proposed. An HS-SFLA algorithm is designed by taking the advantages of HS and SFLA algorithms. The hybrid algorithm of HS-SFLA is adopted for dealing with complex function optimization problems, the experimental results show that HS-SFLA outperforms other state-of-the-art intelligence algorithms significantly in terms of global search ability, convergence speed and robustness on 80% of the benchmark functions tested. The HS-SFLA algorithm could directly be applied to all kinds of continuous optimization problems in the real world.

A Hybrid Algorithm Based on Bat-Inspired Algorithm and Differential Evolution for Constrained Optimization Problems

2015 ◽  
Vol 29 (04) ◽  
pp. 1559007 ◽  
Author(s):  
Shengyu Pei ◽  
Aijia Ouyang ◽  
Lang Tong
Keyword(s):  
Differential Evolution ◽  
Constrained Optimization ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Constrained Optimization Problems ◽  
Local Optima ◽  
The Past ◽  
De Algorithm ◽  
Hybrid Mechanism ◽  
Significant Research

How to solve constrained optimization problems (COPs) is a significant research issue and we combine the bat-inspired algorithm (BA) with differential evolution (DE) into a new hybrid algorithm called BA-DE for solving the COPs. Traditional BAs are prone to sink into stagnation or local optima when no bat individual founds a better location than the past locations for several generations. DE is adopted for updating the past location of bat individuals to force BA to jump out of stagnation or local optima, since it has a great local searching capability. The performance of BA-DE algorithm is improved by the proposed hybrid mechanism. We use 24 well-known benchmark functions to verify the overall performance of our proposed algorithm. Comparisons show that BA-DE outperforms most advanced methods in terms of the final solution's quality.

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