scholarly journals A Customized Differential Evolutionary Algorithm for Bounded Constrained Optimization Problems

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Wali Khan Mashwani ◽  
Zia Ur Rehman ◽  
Maharani A. Bakar ◽  
Ismail Koçak ◽  
Muhammad Fayaz
Keyword(s):  
Evolutionary Algorithms ◽  
Constrained Optimization ◽  
Evolutionary Algorithm ◽  
Evolutionary Computation ◽  
Large Scale ◽  
Optimization Problems ◽  
Science And Engineering ◽  
De Algorithm ◽  
Differential Evolutionary Algorithm ◽  
Nature Inspired Algorithms

Bound-constrained optimization has wide applications in science and engineering. In the last two decades, various evolutionary algorithms (EAs) were developed under the umbrella of evolutionary computation for solving various bound-constrained benchmark functions and various real-world problems. In general, the developed evolutionary algorithms (EAs) belong to nature-inspired algorithms (NIAs) and swarm intelligence (SI) paradigms. Differential evolutionary algorithm is one of the most popular and well-known EAs and has secured top ranks in most of the EA competitions in the special session of the IEEE Congress on Evolutionary Computation. In this paper, a customized differential evolutionary algorithm is suggested and applied on twenty-nine large-scale bound-constrained benchmark functions. The suggested C-DE algorithm has obtained promising numerical results in its 51 independent runs of simulations. Most of the 2013 IEEE-CEC benchmark functions are tackled efficiently in terms of proximity and diversity.

Composite Evolutionary Algorithm for Constrained Optimization

2012 ◽  
Vol 220-223 ◽  
pp. 2846-2851
Author(s):  
Si Lian Xie ◽  
Tie Bin Wu ◽  
Shui Ping Wu ◽  
Yun Lian Liu
Keyword(s):  
Evolutionary Algorithms ◽  
Constrained Optimization ◽  
Evolutionary Algorithm ◽  
Selection Criteria ◽  
Optimization Problems ◽  
Experimental Results ◽  
Constrained Optimization Problems ◽  
Mutation Operators ◽  
Single Operator ◽  
Algorithm Framework

Evolutionary algorithms are amongst the best known methods of solving difficult constrained optimization problems, for which traditional methods are not applicable. Due to the variability of characteristics in different constrained optimization problems, no single evolutionary with single operator performs consistently over a range of problems. We introduce an algorithm framework that uses multiple search operators in each generation. A composite evolutionary algorithm is proposed in this paper and combined feasibility rule to solve constrained optimization problems. The proposed evolutionary algorithm combines three crossover operators with two mutation operators. The selection criteria based on feasibility of individual is used to deal with the constraints. The proposed method is tested on five well-known benchmark constrained optimization problems, and the experimental results show that it is effective and robust

Memetic Algorithm with Constrained Local Search for Large-Scale Global Optimization

2017 ◽  
Vol 26 (2) ◽  
pp. 287-300
Author(s):  
Shikha Mehta
Keyword(s):  
Global Optimization ◽  
Local Search ◽  
Evolutionary Computation ◽  
Large Scale ◽  
Optimization Problems ◽  
Memetic Algorithms ◽  
Search Mechanism ◽  
Global Optima ◽  
Nature Inspired Algorithms ◽  
Single Objective

AbstractNature-inspired algorithms are seen as potential tools to solve large-scale global optimization problems. Memetic algorithms (MAs) are nature-inspired techniques based on evolutionary computation. MAs are considered as modified genetic algorithms integrated with a local search mechanism. Conventional MAs perform well for small dimensions; however, their performance starts declining with the increase in dimensions. It is popularly known as the “curse of dimensionality” problem. In order to solve this problem, MA with constrained local search (MACLS) is proposed for single-objective optimization problems. MACLS restricts the local search to be performed after every generation. Controlled local search enhances the optimization capability of the MA. MACLS has been evaluated with respect to GS-MPSO (the latest modification of MA) and MLCC, EPUS-PSO, JDEdynNP-F, MTS, DewSAcc, DMS-PSO, LSEDA-gl, UEP, ALPSEA, classical DE (differential evolution), and real-coded CHC algorithms that participated in the Congress on Evolutionary Computation 2008 competition. The results establish that MACLS significantly outperforms these algorithms in attaining global optima for unimodal and multimodal single-objective optimization problems for small as well as large dimensions.

Knowledge-Base Constrained Optimization Evolutionary Algorithm and its Applications

2014 ◽  
Vol 536-537 ◽  
pp. 476-480 ◽  
Author(s):  
Wen Long
Keyword(s):  
Evolutionary Algorithms ◽  
Constrained Optimization ◽  
Evolutionary Algorithm ◽  
Knowledge Base ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Engineering Optimization ◽  
Constrained Optimization Problems ◽  
Single Constraint ◽  
Handling Technique

The most existing constrained optimization evolutionary algorithms (COEAs) for solving constrained optimization problems (COPs) only focus on combining a single EA with a single constraint-handling technique (CHT). As a result, the search ability of these algorithms could be limited. Motivated by these observations, we propose an ensemble method which combines different style of EA and CHT from the EA knowledge-base and the CHT knowledge-base, respectively. The proposed method uses two EAs and two CHTs. It randomly combines them to generate novel offspring individuals during each generation. Simulations and comparisons based on four benchmark COPs and engineering optimization problem demonstrate the effectiveness of the proposed approach.

scholarly journals Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 146
Author(s):  
Aleksei Vakhnin ◽  
Evgenii Sopov
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithms ◽  
Numerical Experiments ◽  
Large Scale ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fixed Number ◽  
High Dimensional ◽  
Cooperative Coevolution ◽  
Large Scale Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

scholarly journals Evolutionary Algorithms for Constrained Parameter Optimization Problems

1996 ◽  
Vol 4 (1) ◽  
pp. 1-32 ◽  
Author(s):  
Zbigniew Michalewicz ◽  
Marc Schoenauer
Keyword(s):  
Nonlinear Programming ◽  
Evolutionary Algorithms ◽  
Evolutionary Computation ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Test Cases ◽  
Nonlinear Constraints ◽  
Nonlinear Programming Problem ◽  
General Nonlinear Programming

Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently have several methods been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; however, these methods have several drawbacks, and the experimental results on many test cases have been disappointing. In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem; (2) survey several approaches that have emerged in the evolutionary computation community; and (3) provide a set of 11 interesting test cases that may serve as a handy reference for future methods.

scholarly journals Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems

2021 ◽  
Vol 6 (4 (114)) ◽  
pp. 6-14
Author(s):  
Maan Afathi
Keyword(s):  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Fuzzy Logic Control ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Function Optimization ◽  
Evolutionary Computations ◽  
Hybrid Evolutionary Algorithm ◽  
Logic Control

The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function optimization. Fuzzy logic is applied to switch dynamically between evolutionary algorithms, in an attempt to improve the algorithm performance. The HEF hybrid evolutionary algorithms are compared to GA, PSO, GAPSO, and PSOGA. The comparison uses a variety of measurement functions. In addition to strongly convex functions, these functions can be uniformly distributed or not, and are valuable for evaluating our approach. Iterations of 500, 1000, and 1500 were used for each function. The HEF algorithm’s efficiency was tested on four functions. The new algorithm is often the best solution, HEF accounted for 75 % of all the tests. This method is superior to conventional methods in terms of efficiency

Evolutionary Approaches and Their Applications to Distributed Systems

2010 ◽  
pp. 114-149 ◽  
Author(s):  
Thomas Weise ◽  
Raymond Chiong
Keyword(s):  
Distributed Systems ◽  
Evolutionary Algorithms ◽  
Network Topology ◽  
Large Scale ◽  
Computational Time ◽  
Distributed Environment ◽  
Rapid Changes ◽  
Np Complete ◽  
Nature Inspired Algorithms ◽  
Ubiquitous Presence

The ubiquitous presence of distributed systems has drastically changed the way the world interacts, and impacted not only the economics and governance but also the society at large. It is therefore important for the architecture and infrastructure within the distributed environment to be continuously renewed in order to cope with the rapid changes driven by the innovative technologies. However, many problems in distributed computing are either of dynamic nature, large scale, NP complete, or a combination of any of these. In most cases, exact solutions are hardly found. As a result, a number of intelligent nature-inspired algorithms have been used recently, as these algorithms are capable of achieving good quality solutions in reasonable computational time. Among all the nature-inspired algorithms, evolutionary algorithms are considerably the most extensively applied ones. This chapter presents a systematic review of evolutionary algorithms employed to solve various problems related to distributed systems. The review is aimed at providing an insight of evolutionary approaches, in particular genetic algorithms and genetic programming, in solving problems in five different areas of network optimization: network topology, routing, protocol synthesis, network security, and parameter settings and configuration. Some interesting applications from these areas will be discussed in detail with the use of illustrative examples.

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