Comparative Study of Constrained Handling Techniques of Constrained Differential Evolution Algorithms Applied to Constrained Optimization Problems in Mechanical Engineering

Muangkote, Nipotepat; Photong, Lersak; Sukprasert, Anupong

doi:10.1109/times-icon.2018.8621654

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…On the other hand, there are many constraint handling techniques in the literature (Deb (2000), Cello (2002), Muangkote et al (2019)). In their work, the augmented Lagrangian method is used in which an unconstrained optimization problem is obtained from a constrained optimization one, Long et al (2013), Bahreininejad (2019)).…”

Section: The Proposed Methodologymentioning

confidence: 99%

A Novel Multi-Objective Nonlinear Discrete Binary Gaining-Sharing Knowledge-Based Optimization Algorithm

Hassan

Agrawal

Ganesh

et al. 2022

International Journal of Applied Metaheuristic Computing

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GSK algorithm is based on the concept of how humans acquire and share knowledge through their lifespan. Discrete Binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (DBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable BGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Besides, one of these practical applications is to optimally schedule the flights for residual stranded citizens due to COVID-19. The problem is defined for a decision maker who wants to schedule a multiple stepped trip for a subset of candidate airports to return the maximum number of residuals of stranded citizens remaining in listed airports while comprising the minimization of the total travelled distances for a carrying airplane. A nonlinear binary mathematical programming model for the problem is introduced with a real application case study, the case study is solved using (DBGSK).

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Section: The Proposed Methodologymentioning

confidence: 99%

A Novel Multi-Objective Nonlinear Discrete Binary Gaining-Sharing Knowledge-Based Optimization Algorithm

Hassan

Agrawal

Ganesh

et al. 2022

International Journal of Applied Metaheuristic Computing

View full text Add to dashboard Cite

show abstract

“…On the other hand, there are many constraint handling techniques in the literature (Deb [33]; Cello [34]; Muangkote et al [35]. In this paper, the augmented Lagrangian method is used to handle the constraints, in which a constrained optimization problem is converted into an unconstrained optimization problem (Long et al [36]; Bahreininejad [37]).…”

Section: Proposed Methodologymentioning

confidence: 99%

Stochastic Travelling Advisor Problem Simulation with a Case Study: A Novel Binary Gaining-Sharing Knowledge-Based Optimization Algorithm

et al. 2020

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This article proposes a new problem which is called the Stochastic Travelling Advisor Problem (STAP) in network optimization, and it is defined for an advisory group who wants to choose a subset of candidate workplaces comprising the most profitable route within the time limit of day working hours. A nonlinear binary mathematical model is formulated and a real application case study in the occupational health and safety field is presented. The problem has a stochastic nature in travelling and advising times since the deterministic models are not appropriate for such real-life problems. The STAP is handled by proposing suitable probability distributions for the time parameters and simulating the problem under such conditions. Many application problems like this one are formulated as nonlinear binary programming models which are hard to be solved using exact algorithms especially in large dimensions. A novel binary version of the recently developed gaining-sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems is given. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. The binary version of GSK (BGSK) depends mainly on two stages that enable BGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. The generated simulation runs of the example are solved using the BGSK, and the output histograms and the best-fitted distributions for the total profit and for the route length are obtained.

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“…To solve the constrained optimization problem, several techniques have been applied to metaheuristic algorithms [47][48][49]. Of these, maintaining feasibility of solutions and penalty approaches are most common and popular approaches.…”

Section: Constraint Handling Approachmentioning

confidence: 99%

Optimum Scheduling the Electric Distribution Substations with a Case Study: An Integer Gaining-Sharing Knowledge-Based Metaheuristic Algorithm

et al. 2020

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This work is dedicated to the economic scheduling of the required electric stations in the upcoming 10-year long-term plan. The calculation of the required electric stations is carried out by estimating the yearly consumption of electricity over a long-time plan and then determining the required number of stations. The aim is to minimize the total establishing and operating costs of the stations based on a mathematical programming model with nonlinear objective function and integer decision variables. The introduced model is applied for a real practical case study to conclude the number of yearly constructed stations over a long-term plan in the electricity sector in Jeddah City, Saudi Arabia. The current planning method is based only on intuition by constructing the same number of required stations in each year without searching for better solutions. To solve the introduced mathematical model, a novel recent gaining sharing knowledge-based algorithm, named GSK, has been used. The Augmented Lagrangian Method (ALM) is applied to transform the constrained formulation to become unconstrained with penalization to the objective function. According to the obtained results of the real case study, the proposed GSK with ALM approved an ability to solve this case with respect to convergence, efficiency, quality, and robustness.

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Comparative Study of Constrained Handling Techniques of Constrained Differential Evolution Algorithms Applied to Constrained Optimization Problems in Mechanical Engineering

Cited by 8 publications

References 13 publications

A Novel Multi-Objective Nonlinear Discrete Binary Gaining-Sharing Knowledge-Based Optimization Algorithm

A Novel Multi-Objective Nonlinear Discrete Binary Gaining-Sharing Knowledge-Based Optimization Algorithm

Stochastic Travelling Advisor Problem Simulation with a Case Study: A Novel Binary Gaining-Sharing Knowledge-Based Optimization Algorithm

Optimum Scheduling the Electric Distribution Substations with a Case Study: An Integer Gaining-Sharing Knowledge-Based Metaheuristic Algorithm

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