A Genetic Algorithm Based Augmented Lagrangian Method for Computationally Fast Constrained Optimization

Srivastava, Soumil; Deb, Kalyanmoy

doi:10.1007/978-3-642-17563-3_40

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Srivastava and Deb [20] present a genetic algorithm based augmented Lagrangian method for constrained optimization. Their algorithm relies on large populations and an effective local search procedure.…”

Section: Background and Related Workmentioning

confidence: 99%

Towards an Augmented Lagrangian Constraint Handling Approach for the (1+1)-ES

Arnold

Porter

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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We consider the problem of devising an approach for handling inequality constraints in evolution strategies that allows converging linearly to optimal solutions on sphere functions with a single linear constraint. An analysis of the single-step behaviour of the (1 + 1)-ES shows that the task of balancing improvements in the objective with those in the constraint function is quite delicate, and that adaptive approaches need to be carefully designed in order to avoid failure. Based on the understanding gained, we propose a simple augmented Lagrangian approach and experimentally demonstrate good performance on a broad range of sphere functions as well as on moderately ill-conditioned ellipsoids with a single linear constraint.

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Section: Background and Related Workmentioning

confidence: 99%

Towards an Augmented Lagrangian Constraint Handling Approach for the (1+1)-ES

Arnold

Porter

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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“…But, this may be an ineffective way, in particular, for problems where feasible solutions are difficult to be found. Based on the good results obtained by population-based algorithms that rely on augmented Lagrangian penalties, very promising frameworks seem to emerge for industrial applications [17,49,50,53,55].…”

Section: Algorithm 1 Genetic Algorithmmentioning

confidence: 99%

Evaluation of optimality in the fuzzy single machine scheduling problem including discounted costs

Bentrcia

Mouss

et al. 2015

Int J Adv Manuf Technol

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The single machine scheduling problem has been often regarded as a simplified representation that contains many polynomial solvable cases. However, in real-world applications, the imprecision of data at the level of each job can be critical for the implementation of scheduling strategies. Therefore, the single machine scheduling problem with the weighted discounted sum of completion times is treated in this paper, where we assume that the processing times, weighting coefficients and discount factor are all described using trapezoidal fuzzy numbers. Our aim in this study is to elaborate adequate measures in the context of possibility theory for the assessment of the optimality of a fixed schedule. Two optimization approaches namely genetic algorithm and pattern search are proposed as computational tools for the validation of the obtained properties and results. The proposed approaches are experimented on the benchmark problem instances and a sensitivity analysis with respect to some configuration parameters is conducted. Modeling and resolution frameworks considered in this research offer promise to deal with optimality in the wide class of fuzzy scheduling problems, which is recognized to be a difficult task by both researchers and practitioners.

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“…Penalty function method is quite popular and easy to implement, but there are some drawbacks [29]. The main one is that it requires a careful tuning of penalty factors, which limits its precision and speed.…”

Section: Introductionmentioning

confidence: 99%

An improved artificial bee colony algorithm for solving constrained optimization problems

Liang

Wan

Fang

2015

Int. J. Mach. Learn. & Cyber.

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The artificial bee colony (ABC) algorithm is a global stochastic optimization algorithm inspired by simulating the foraging behavior of honey bees. It has been successfully applied to solve the constrained optimization problems (COPs) with a constraint handling technique (Deb's rules). However, it may also lead to premature convergence. In order to improve this problem, we propose an improved artificial bee colony (I-ABC) algorithm for COPs. In I-ABC algorithm, we firstly relax the Deb's rules by introducing the approximate feasible solutions to suitably utilize the information of the infeasible solutions with better objective function value and small violation. Next, we construct a selection strategy based on rank selection and design a search mechanism using the information of the best-so-far solution to balance the exploration and the exploitation at different stages. In addition, periodic boundary handling mode is used to repair invalid solutions. To verify the performance of I-ABC algorithm, 24 benchmark problems are employed and two comparison experiments have been carried out. The numerical results show that the proposed I-ABC algorithm has an outstanding performance for the COPs.

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A Genetic Algorithm Based Augmented Lagrangian Method for Computationally Fast Constrained Optimization

Cited by 5 publications

References 4 publications

Towards an Augmented Lagrangian Constraint Handling Approach for the (1+1)-ES

Towards an Augmented Lagrangian Constraint Handling Approach for the (1+1)-ES

Evaluation of optimality in the fuzzy single machine scheduling problem including discounted costs

An improved artificial bee colony algorithm for solving constrained optimization problems

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