FSB-EA: Fuzzy search bias guided constraint handling technique for evolutionary algorithm

Li, Zhiyong; Chen, Shaomiao; Zhang, Shiwen; Jiang, Shengming; Gu, Yu; Nouioua, Mourad

doi:10.1016/j.eswa.2018.10.035

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…Constraint optimization problems often produce infeasible solutions 23 . One of the major issues in constraint optimization is the process of selecting appropriate infeasible solutions to continue evolving solutions throughout the search process 35 . In our approach, the constraint violation of a solution is defined as

ϕ = {falsefalse}_{\sum}^{i = 1} B R false(i false) - R B,

where

B R false(i false)

is the bandwidth requirement of user

i

and

R B

is the remaining bandwidth request to users by the solution.…”

Section: Evospo‐dtn Framework and Methodologymentioning

confidence: 99%

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An evolutive framework for server placement optimization to digital twin networks

Xiao

Han

Weng

et al. 2023

Int J Communication

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SummaryDigital twin network (DTN) is a foremost enabler for efficient optimization in modern networks, as it owns massive real‐time data and requires interaction with the physical network in real‐time. When constructing a DTN, it is necessary to deploy many servers in the physical network for digital models' storage, calculation, and communication. Evolutionary algorithms show outstanding global optimization capabilities compared to the constructive heuristic method in such an optimization problem. However, due to the high dimensionality of the problem and the complicated evaluation of the deployment plan, evolutionary algorithms easily fall into the optimum local at a high computational cost, given that the server placement problem is an NP‐hard combinatorial optimization problem. In this research, we propose an evolutionary framework for server layout optimization that significantly improves the optimization efficiency of evolutionary algorithms and reduces the algorithm's computational cost. An offline‐learning‐based approach is used to reduce the search space, and a self‐examining guided local search method is proposed to improve the search efficiency. Additionally, a look‐up table‐based hybrid approach is used for solution evaluation, reducing computational overhead. Experimental results show that the proposed framework and optimization strategy can significantly improve the evolutionary algorithm search efficiency and achieve excellent convergence performance.

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ϕ = {falsefalse}_{\sum}^{i = 1} B R false(i false) - R B,

where

B R false(i false)

is the bandwidth requirement of user

i

and

R B

is the remaining bandwidth request to users by the solution.…”

Section: Evospo‐dtn Framework and Methodologymentioning

confidence: 99%

“…23 One of the major issues in constraint optimization is the process of selecting appropriate infeasible solutions to continue evolving solutions throughout the search process. 35 In our approach, the constraint violation of a solution is defined as…”

Section: Constraint's Handling and Selectionmentioning

confidence: 99%

An evolutive framework for server placement optimization to digital twin networks

Xiao

Han

Weng

et al. 2023

Int J Communication

Self Cite

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“…In Li et al (2019b) 2020) introduced a feasibility-rule-based CHT named FROFI, integrated into a radial basis function (RBF) surrogate-assisted DE algorithm, and applied to 2 test problems and 2 reservoir models. FROFI integrates information from the objective function through the DE algorithm operators, along with a replacement procedure, into the well-known Deb rules, to achieve a more efficient balance between objective function and constraints.…”

Section: Techniques Based On Separation Of Objective and Constraintsmentioning

confidence: 99%

Constraint handling techniques for metaheuristics: a state-of-the-art review and new variants

et al. 2023

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Metaheuristic optimization algorithms (MOAs) are computational randomized search processes which draw inspiration from physical and biological phenomena, with an application spectrum that extends to numerous fields, ranging from engineering design to economics. MOAs were originally developed for solving unconstrained NP-complete problems, and hence their application to constrained optimization problems (COPs) requires the implementation of specialized techniques that facilitate the treatment of performance and bound constraints. While considerable research efforts have been oriented towards the development and subsequent enhancement of novel constraint handling techniques (CHTs) for MOAs, a systematic review of such techniques has not been conducted hitherto. This work presents a state-of-the-art review on CHTs used with MOAs and proposes eight novel variants based on the feasibility rules and ε-constrained techniques. The distinctive feature of the new variants is that they consider the level and number of constraint violations, besides the objective function value, for selection of individuals within a population. The novel variant performance is evaluated and compared with that of four well-known CHTs from the literature using the metaheuristic pity beetle algorithm, based upon 20 single-objective benchmark COPs. The computational results highlight the accuracy, effectiveness, and versatility of the novel variants, as well as their performance superiority in comparison with existing techniques, stemming from their distinctive formulation. The complete code can be downloaded from GitHub (https://github.com/nikoslagaros/MOAs-and-CHTs).

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“…The main purpose of the solution process in optimization problems is to minimize or maximize the performance, duration, efficiency, and productivity parameters. Most of the optimization problems in real-world contain some constraints defined on decision variables [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Comparison of metaheuristic optimization algorithms with a new modified deb feasibility constraint handling technique

Çimen¹,

Garip²,

Boz³

2021

Turk J Elec Eng & Comp Sci

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In this study, the modification of the Deb Feasibility method is considered to solve the constrained optimization problems. In the developed Modified Deb Feasibility constraint method, the third rule in its procedure was revised in order to increase the performance of the Deb feasibility constraint handling method. The innovation in the method is based on generating a new individual by using both possible solutions that violate the constraints in the method used for solving the problem. In detail, discussions were given about the application and usefulness of six constrained handling techniques. Furthermore, Genetic Algorithm, Particle Swarm Optimization, Harris Hawks Optimization, Whale Optimization Algorithm, Grey Wolf Optimization and Sine Cosine algorithms were applied to both various benchmark functions and also different engineering application problems such as pressure vessel design, welded beam design, speed reducer design and active filter design. Overall the experimental results show that Modified Deb Feasibility constraint handling technique is more robust and efficient than Deb Feasibility technique and most of the other constraint handling techniques.

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FSB-EA: Fuzzy search bias guided constraint handling technique for evolutionary algorithm

Cited by 10 publications

References 23 publications

An evolutive framework for server placement optimization to digital twin networks

An evolutive framework for server placement optimization to digital twin networks

Constraint handling techniques for metaheuristics: a state-of-the-art review and new variants

Comparison of metaheuristic optimization algorithms with a new modified deb feasibility constraint handling technique

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