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An enhanced multi-operator differential evolution algorithm for tackling knapsack optimization problem

NEURAL COMPUTING & APPLICATIONS 2023年第18期35卷 13359-13386页

作者： Sallam, Karam M. Abohany, Amr A. Rizk-Allahi, Rizk M. Univ Canberra Fac Sci & Technol Sch IT & Syst Bruce ACT 2601 Australia Kafrelsheikh Univ Fac Comp & Informat Kafr El Shaikh Egypt Menoufia Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm 32511 Egypt

The knapsack problem (KP) is a discrete combinatorial optimization problem that has different utilities in many fields. It is described as a non-polynomial time (NP) problem and has several applications in many fields. The differential evolution (DE) algorithm has been successful in solving continuous optimization problems, but it needs further work to solve discrete and binary optimization problems and avoid local optima. According to the literature, no DE search operator or algorithm is optimal for all optimization tasks. As a result, using more than one search operator in a single algorithm architecture, called multi-operator-based algorithms, is a solution to address this problem. These methods outperformed single-based methods for continuous optimization problems. Thus, in this paper, a binary multi-operator differential evolution (BMODE) approach is presented to tackle the 0-1 KP. The presented methodology utilizes multiple differential evolution (DE) mutation strategies with complementary characteristics, with the best mutation operator being asserted utilizing the produced solutions' quality and the population's diversity. In BMODE, two types of transfer functions (TFs) (S-shaped and V-shaped) are used to transfer the continuous solutions to binary ones to be able to calculate the fitness function value. To handle the capacity constraints, a feasibility rule is utilized and some of the infeasible solutions are repaired. The performance of BMODE is tested by solving 40 instances with multiple dimensions, i.e., low, medium, and high. Experimental results of the proposed BMODE are compared with well-known state-of-the-art 0-1 knapsack algorithms. Based on Wilcoxon's nonparametric statistical test (alpha=0.05), the proposed BMODE can obtain the best results against the rival algorithms in most cases, and can work well on stability and computational accuracy.

关键词： Evolutionary algorithms Differential evolution multi-operator algorithms Binary optimization Knapsack problem

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Testing United multi-operator Evolutionary algorithms on the CEC2014 Real-Parameter Numerical Optimization

Testing United Multi-Operator Evolutionary Algorithms on the...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Elsayed, Saber M. Sarker, Ruhul A. Essam, Daryl L. Hamza, Noha M. Univ New S Wales Sch Engn & Informat Technol Canberra ACT Australia

ISBN: (纸本)9781479914883

This paper puts forward a proposal for combining multi-operator evolutionary algorithms (EAs), in which three EAs, each with multiple search operators, are used. During the evolution process, the algorithm gradually emphasizes on the best performing multi-operator EA, as well as the search operator. The proposed algorithm is tested on the CEC2014 single objective real-parameter competition. The results show that the proposed algorithm has the ability to reach good solutions.

关键词： evolutionary algorithms multi-method algorithms multi-operator algorithms

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United multi-operator Evolutionary algorithms

United Multi-Operator Evolutionary Algorithms

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IEEE Congress on Evolutionary Computation (CEC)

作者： Elsayed, Saber M. Sarker, Ruhul A. Essam, Daryl L. Univ New S Wales Sch Informat Technol & Engn Canberra ACT Australia

ISBN: (纸本)9781479914883

multi-method and multi-operator evolutionary algorithms (EAs) have shown superiority to any single EAs with a single operator. To further improve the performance of such algorithms, in this research study, a united multi-operator EAs framework is proposed, in which two EAs, each with multiple search operators, are used. During the evolution process, the algorithm emphasizes on the best performing multi-operator EA, as well as the search operator. The proposed algorithm is tested on a well-known set of constrained problems with 10D and 30D. The results show that the proposed algorithm scales well and is superior to the-state-of-the-art algorithms, especially for the 30D test problems.

关键词： Constrained optimization evolutionary algorithms multi-method algorithms multi-operator algorithms

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Heuristic Embedded Genetic Algorithm for Heterogeneous Project Scheduling Problems

Heuristic Embedded Genetic Algorithm for Heterogeneous Proje...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Mahmud, Firoz Zaman, Forhad Sarker, Ruhul Essam, Daryl Univ New South Wales Sch Engn & Informat Technol Canberra ACT Australia

ISBN: (纸本)9781728169293

Over the last few decades, many solution approaches have been developed for solving different variants of resource-constrained project scheduling problems (RCPSPs). In most of them, it is assumed that a project consists of some homogeneous activities that require all types of resources over the entire project horizon. On the contrary, many real-world projects consist of heterogeneous activities that use different types of resources at different time instants during the project execution. The application of existing approaches, developed for RCPSPs with homogeneous activities, in solving RCPSPs with heterogeneous activities is computationally expensive. In this paper, we propose a heuristic embedded genetic algorithm to address RCPSPs with heterogeneous activities. Two heuristics are proposed to obtain high-quality feasible solutions. The first heuristic is based on priority rules while the second one based on a new neighbourhood swapping matrix. To evaluate the performance of the proposed algorithm, we solve a number of real-world and modified test problems, and the obtained results are compared with an existing algorithm. It is found that the proposed approach obtains high-quality solutions with a significantly lower computational time compared to other algorithms.

关键词： heterogeneous project scheduling problems evolutionary algorithms multi-operator algorithms

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A self-adaptive combined strategies algorithm for constrained optimization using differential evolution

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APPLIED MATHEMATICS AND COMPUTATION 2014年 241卷 267-282页

作者： Elsayed, Saber M. Sarker, Ruhul A. Essam, Daryl L. Univ New S Wales Sch Engn & Informat Technol Canberra ACT Australia

There are a huge number of differential evolution variants that have been proposed in the literature for solving constrained problems. However, none of them was considered as being a well-accepted approach for solving a broad range of problems with different mathematical properties. Therefore, in this paper, for a better coverage of the problem characteristics, a self-adaptive differential evolution algorithm is introduced. To do that, it uses multiple search operators in conjunction with multiple constraint handling techniques. The need for such an approach is justified by experimental analysis on a well-known set of problems. The results show that the proposed algorithm is superior to other state-of-the-art algorithms. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Differential evolution Constrained optimization Evolutionary algorithms multi-operator algorithms

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Adaptive Configuration of evolutionary algorithms for constrained optimization

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APPLIED MATHEMATICS AND COMPUTATION 2013年 222卷 680-711页

作者： Elsayed, Saber M. Sarker, Ruhul A. Essam, Daryl L. Univ New S Wales Sch Engn & Informat Technol Canberra ACT Australia

In the literature, many different evolutionary algorithms (EAs) with different search operators have been reported for solving optimization problems. However, no single algorithm is consistently able to solve all types of problems. To overcome this problem, the recent trend is to use a mix of operators within a single algorithm. There are also cases where multiple methodologies, each with a single search operator, have been used under one approach. These approaches outperformed the single operator based single algorithm approaches. In this paper, we propose a new algorithm framework that uses multiple methodologies, where each methodology uses multiple search operators. We introduce it as the EA with Adaptive Configuration, where the first level is to decide the methodologies and the second level is to decide the search operators. In this approach, all operators and population sizes are updated adaptively. Although the framework may sound complex, one can gain significant benefits from it in solving optimization problems. The proposed framework has been tested by solving two sets of specialized benchmark problems. The results showed a competitive, if not better, performance when it was compared to the state-of-the-art algorithms. Moreover, the proposed algorithm significantly reduces the computational time in comparison to both single and multi-operator based algorithms. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Constrained optimization Evolutionary algorithms multi-method algorithms multi-operator algorithms

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