A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems

Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO), and bat algorithm (BA) have gained much popularity and they have successfully tackled various test suites of benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm (MSIA) is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation (IEEE-CEC′13). The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems.

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Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

10.21203/rs.3.rs-277857/v1 ◽

2021 ◽

Author(s):

Mohammad Shehab ◽

Laith Abualigah

Keyword(s):

Real World ◽

Optimization Problems ◽

Second Stage ◽

Opposition Based Learning ◽

High Fitness ◽

Fitness Value ◽

Speed Up ◽

Disruption Operator ◽

The Given ◽

Real World Problems

Abstract Multi-Verse Optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step using Opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems, and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.

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SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

Mathematics ◽

10.3390/math7030250 ◽

2019 ◽

Vol 7 (3) ◽

pp. 250 ◽

Cited By ~ 7

Author(s):

Umesh Balande ◽

Deepti Shrimankar

Keyword(s):

Constrained Optimization ◽

Engineering Design ◽

Real World ◽

Optimization Design ◽

Firefly Algorithm ◽

Optimization Problems ◽

Design Problems ◽

Improved Firefly Algorithm ◽

Engineering Design Problems ◽

And Performance

Firefly-Algorithm (FA) is an eminent nature-inspired swarm-based technique for solving numerous real world global optimization problems. This paper presents an overview of the constraint handling techniques. It also includes a hybrid algorithm, namely the Stochastic Ranking with Improved Firefly Algorithm (SRIFA) for solving constrained real-world engineering optimization problems. The stochastic ranking approach is broadly used to maintain balance between penalty and fitness functions. FA is extensively used due to its faster convergence than other metaheuristic algorithms. The basic FA is modified by incorporating opposite-based learning and random-scale factor to improve the diversity and performance. Furthermore, SRIFA uses feasibility based rules to maintain balance between penalty and objective functions. SRIFA is experimented to optimize 24 CEC 2006 standard functions and five well-known engineering constrained-optimization design problems from the literature to evaluate and analyze the effectiveness of SRIFA. It can be seen that the overall computational results of SRIFA are better than those of the basic FA. Statistical outcomes of the SRIFA are significantly superior compared to the other evolutionary algorithms and engineering design problems in its performance, quality and efficiency.

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A novel chaotic bat algorithm based on catfish effect for engineering optimization problems

Engineering Computations ◽

10.1108/ec-04-2018-0181 ◽

2019 ◽

Vol 36 (5) ◽

pp. 1744-1763

Author(s):

Wensheng Xiao ◽

Qi Liu ◽

Linchuan Zhang ◽

Kang Li ◽

Lei Wu

Keyword(s):

Numerical Experiment ◽

Real World ◽

Optimization Problems ◽

Bat Algorithm ◽

Optimization Method ◽

Engineering Optimization ◽

Local Optima ◽

Content Type ◽

Engineering Design Problems ◽

Engineering Optimization Problems

PurposeBat algorithm (BA) is a global optimization method, but has a worse performance on engineering optimization problems. The purpose of this study is to propose a novel chaotic bat algorithm based on catfish effect (CE-CBA), which can effectively deal with optimization problems in real-world applications.Design/methodology/approachIncorporating chaos strategy and catfish effect, the proposed algorithm can not only enhance the ability for local search but also improve the ability to escape from local optima traps. On the one hand, the performance of CE-CBA has been evaluated by a set of numerical experiment based on classical benchmark functions. On the other hand, five benchmark engineering design problems have been also used to test CE-CBA.FindingsThe statistical results of the numerical experiment show the significant improvement of CE-CBA compared with the standard algorithms and improved bat algorithms. Moreover, the feasibility and effectiveness of CE-CBA in solving engineering optimization problems are demonstrated.Originality/valueThis paper proposed a novel BA with two improvement strategies including chaos strategy and catfish effect for the first time. Meanwhile, the proposed algorithm can be used to solve many real-world engineering optimization problems with several decision variables and constraints.

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Bat algorithm applied to continuous constrained optimization problems

Journal of Information and Optimization Sciences ◽

10.1080/02522667.2019.1694740 ◽

2020 ◽

pp. 1-19

Author(s):

Ioannis Karakonstantis ◽

Aristidis Vlachos

Keyword(s):

Constrained Optimization ◽

Optimization Problems ◽

Bat Algorithm ◽

Constrained Optimization Problems

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Constrained optimization of line-start PM motor based on the gray wolf optimizer

Eksploatacja i Niezawodnosc - Maintenance and Reliability ◽

10.17531/ein.2021.1.1 ◽

2021 ◽

Vol 23 (1) ◽

pp. 1-10

Author(s):

Łukasz Knypiński

Keyword(s):

Constrained Optimization ◽

Optimization Algorithm ◽

Computer Software ◽

Optimization Procedure ◽

Bat Algorithm ◽

Linear Constraint ◽

Gray Wolf ◽

Good Convergence ◽

Design Variables ◽

Pm Synchronous Motor

This paper presents the algorithm and computer software for constrained optimization based on the gray wolf algorithm. The gray wolf algorithm was combined with the external penalty function approach. The optimization procedure was developed using Borland Delphi 7.0. The developed procedure was then applied to design of a line-start PM synchronous motor. The motor was described by three design variables which determine the rotor structure. The multiplicative compromise function consisted of three maintenance parameters of designed motor and one non-linear constraint function was proposed. Next, the result obtained for the developed procedure (together with the gray wolf algorithm) was compared with results obtained using: (a) the particle swarm optimization algorithm, (b) the bat algorithm and (c) the genetic algorithm. The developed optimization algorithm is characterized by good convergence, robustness and reliability. Selected results of the computer simulation are presented and discussed.

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COLSHADE for Real-World Single-Objective Constrained optimization Problems

2020 IEEE Congress on Evolutionary Computation (CEC) ◽

10.1109/cec48606.2020.9185583 ◽

2020 ◽

Author(s):

Javier Gurrola-Ramos ◽

Arturo Hernandez-Aguirre ◽

Oscar Dalmau-Cedeno

Keyword(s):

Constrained Optimization ◽

Real World ◽

Optimization Problems ◽

Constrained Optimization Problems ◽

Single Objective

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Multi-Operator Differential Evolution Algorithm for Solving Real-World Constrained Optimization Problems

2020 IEEE Congress on Evolutionary Computation (CEC) ◽

10.1109/cec48606.2020.9185722 ◽

2020 ◽

Cited By ~ 1

Author(s):

Karam M. Sallam ◽

Saber M. Elsayed ◽

Ripon K. Chakrabortty ◽

Michael J. Ryan

Keyword(s):

Differential Evolution ◽

Constrained Optimization ◽

Real World ◽

Optimization Problems ◽

Differential Evolution Algorithm ◽

Constrained Optimization Problems ◽

Evolution Algorithm

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A Customized Differential Evolutionary Algorithm for Bounded Constrained Optimization Problems

Complexity ◽

10.1155/2021/5515701 ◽

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.

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