Parameterless penalty function for solving constrained evolutionary optimization

Jadaan, Omar Al; Rajamani, Lakshmi; Rao, C. Raghavendra

doi:10.1109/hima.2009.4937826

Cited by 14 publications

(8 citation statements)

References 14 publications

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“…4) Figures( 1, 2, 3) are drawn for groups 1, 2, and 3, respectively. Figures ( 4,5,6,7,8,9) provide the Average Makespan and Flowtime values for groups 1, 2, and 3, respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Evolutionary process is accomplished by applying Rankbased Roulette Wheel Selection (RRWS) [7], [8], [2], [3], Crossover and Mutation Operators from one generation to the next, and Selection Operator which determines how many and which individuals will be kept in the next generation. Crossover Operator controls how to exchange genes between individuals, while the Mutation Operator allows for random gene alteration of an individual.…”

Section: Evolutionary Processmentioning

confidence: 99%

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Rank Based Genetic Scheduler for Grid Computing Systems

Abdulal

Jabas

Ramachandram

et al. 2010

2010 International Conference on Computational Intelligence and Communication Networks

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Computational grids have become attractive and promising platforms for solving large-scale high-performance applications of multi-institutional interest. However, the management of resources and computational tasks is a critical and complex undertaking as these resources and tasks are geographically distributed and a heterogeneous in nature. This paper proposes a novel Rank Based Genetic Scheduler for Grid Computing Systems (RGSGCS) for scheduling independent tasks in the grid environment by minimizing Makespan and Flowtime. The novel RGSGCS speeds up convergence and shortens the search time better than Standard Genetic Algorithm (SGA) using Rank-based fitness, at the same time the heuristic initialization of initial population using Minimum Completion Time (MCT) heuristic which allows RGSGCS to obtain a high quality feasible scheduling solution. The simulation results show that RGSGCS has better search time than SGA.

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“…4) Figures( 1, 2, 3) are drawn for groups 1, 2, and 3, respectively. Figures ( 4,5,6,7,8,9) provide the Average Makespan and Flowtime values for groups 1, 2, and 3, respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Evolutionary Processmentioning

confidence: 99%

Rank Based Genetic Scheduler for Grid Computing Systems

Abdulal

Jabas

Ramachandram

et al. 2010

2010 International Conference on Computational Intelligence and Communication Networks

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“…The method adaptively updates different penalty parameter for each constraint. Some extensions and applications of penalty parameter less approach can be found in Liao (2010), Manoharan et al (2008), Jadaan et al (2009) and Jan and Khanum (2012).…”

Section: Related Studiesmentioning

confidence: 97%

Uniform adaptive scaling of equality and inequality constraints within hybrid evolutionary-cum-classical optimization

Datta

Deb

2015

Soft Comput

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The holy grail of constrained optimization is the development of an efficient, scale invariant and generic constraint handling procedure. To address these, the present paper proposes a unified approach of constraint handling, which is capable of handling all inequality, equality and hybrid constraints in a coherent manner. The proposed method also automatically resolves the issue of constraint scaling which is critical in real world and engineering optimization problems. The proposed unified approach converts the single-objective constrained optimization problem into a multi-objective problem. Evolutionary multi-objective optimization is used to solve the modified bi-objective problem and to estimate the penalty parameter automatically. The constrained optimum is further improved using classical optimization. The efficiency of the proposed method is validated on a set of well-studied constrained test problems and compared against without using normalization technique to show the necessity of normalization. The results establish the importance of scaling , especially in constrained optimization and call for further investigation into its use in constrained optimization research.

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“…There are generally two approaches for solving multi‐objective optimization problems: one is to convert the optimization problem into a single‐objective optimization using multi‐criteria decision‐making methods, and then the mature single‐ objective evolutionary algorithm (SOEA) is used to solve the optimization model. The other is a multi‐objective evolutionary algorithm (MOEA) such as the non‐dominated sorting genetic algorithm‐II (NSGA‐II), multi‐objective particle swarm optimization (MOPSO), or others, which are directly used to find a set of optimal solutions …”

Section: Optimization Model For Integrated Production Processmentioning

confidence: 99%

A novel optimization approach for oil and gas production process considering model parameters uncertainties

et al. 2016

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Through analyzing the integrated oil and gas production process, a multi-objective optimization model for the integrated oil and gas production process is established with considering nonlinear reservoir behaviour, multiphase flow in wells and constraints from the surface facilities. In order to reduce the influence of model parameter uncertainty in oil and gas production process, an error compensation method based on Gaussian mixture model (GMM) is proposed to compensate the model. Non-dominated sorting genetic algorithm-II (NSGA-II) is used as the optimization algorithm. Moreover, an operational strategy by using post-optimization is applied to solve the optimization model, so as to ensure the feasibility of obtained optimal set-point. Finally, a novel optimization approach for oil and gas production process considering model parameter uncertainty is proposed. Simulation results indicate that the proposed optimization method is feasible and effective. This article is protected by copyright.All rights reserved

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Parameterless penalty function for solving constrained evolutionary optimization

Cited by 14 publications

References 14 publications

Rank Based Genetic Scheduler for Grid Computing Systems

Rank Based Genetic Scheduler for Grid Computing Systems

Uniform adaptive scaling of equality and inequality constraints within hybrid evolutionary-cum-classical optimization

A novel optimization approach for oil and gas production process considering model parameters uncertainties

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