A novel evolution strategy for constrained optimization in engineering design

Kuşakçı, Ali Osman; Can, Mehmet

doi:10.1109/icat.2013.6684072

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…CMA-ES has been also integrated with ASCHEA, an approach proposed in [51] to adapt the tolerances on the equality constraints [39]. Other approaches rank individuals based on three independent rankings [52], namely objective function, constraint violation, and number of violated constraints, or use surrogate models to learn information about constraints [37], [40]. A repair mechanism was used in a problem-specific variant of CMA-ES for financial optimization [42].…”

Section: A Methods Based On Cma-esmentioning

confidence: 99%

Memetic Viability Evolution for Constrained Optimization

Maesani

Iacca

Floreano

2016

IEEE Trans. Evol. Computat.

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The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while Covariance Matrix Adaptation Evolution Strategy is one of the most efficient algorithms for unconstrained optimization problems, it cannot be readily applied to constrained ones. Here, we used concepts from Memetic Computing, i.e. the harmonious combination of multiple units of algorithmic information, and Viability Evolution, an alternative abstraction of artificial evolution, to devise a novel approach for solving optimization problems with inequality constraints. Viability Evolution emphasizes elimination of solutions not satisfying viability criteria, defined as boundaries on objectives and constraints. These boundaries are adapted during the search to drive a population of local search units, based on Covariance Matrix Adaptation Evolution Strategy, towards feasible regions. These units can be recombined by means of Differential Evolution operators. Of crucial importance for the performance of our method, an adaptive scheduler toggles between exploitation and exploration by selecting to advance one of the local search units and/or recombine them. The proposed algorithm can outperform several state-of-the-art methods on a diverse set of benchmark and engineering problems, both for quality of solutions and computational resources needed.

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Section: A Methods Based On Cma-esmentioning

confidence: 99%

Memetic Viability Evolution for Constrained Optimization

Maesani

Iacca

Floreano

2016

IEEE Trans. Evol. Computat.

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“…An approach performs selection based on three feasibility rules [16]: feasible individuals are compared on objectives, infeasible ones are compared on total constraint violations, and feasible individuals are always ranked before infeasible ones. Similarly, a recently proposed method modifies the ranking of individuals based on three independent rankings: by objective function, by constraint violation amount, and by number of violated constraints depending on if the solution is feasible or infeasible [17]. Other approaches reduce the probability of generating infeasible solutions when in the proximity of the constraint, by moving the mean of the population [18] or by explicitly controlling the step size using a lower bound [7].…”

Section: Related Workmentioning

confidence: 99%

Viability Principles for Constrained Optimization Using a (1+1)-CMA-ES

Maesani

Floreano

2014

Parallel Problem Solving From Nature – PPSN XIII

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Abstract. Viability Evolution is an abstraction of artificial evolution which operates by eliminating candidate solutions that do not satisfy viability criteria. Viability criteria are defined as boundaries on the values of objectives and constraints of the problem being solved. By adapting these boundaries it is possible to drive the search towards desired regions of solution space, discovering optimal solutions or those satisfying a set of constraints. Although in previous work we demonstrated the feasibility of the approach by implementing it on a simple genetic algorithm, the method was clearly not competitive with the current evolutionary computation state-of-the-art. In this work, we test Viability Evolution principles on a modified (1+1)-CMA-ES for constrained optimization. The resulting method shows competitive performance when tested on eight unimodal problems.

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A co-evolutionary algorithm with adaptive penalty function for constrained optimization

de Melo,

Nascimento,

Iacca

2024

Soft Comput

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Several constrained optimization problems have been adequately solved over the years thanks to the advances in the area of metaheuristics. Nevertheless, the question as to which search logic performs better on constrained optimization often arises. In this paper, we present Dual Search Optimization (DSO), a co-evolutionary algorithm that includes an adaptive penalty function to handle constrained problems. Compared to other self-adaptive metaheuristics, one of the main advantages of DSO is that it is able auto-construct its own perturbation logics, i.e., the ways solutions are modified to create new ones during the optimization process. This is accomplished by co-evolving the solutions (encoded as vectors of integer/real values) and perturbation strategies (encoded as Genetic Programming trees), in order to adapt the search to the problem. In addition to that, the adaptive penalty function allows the algorithm to handle constraints very effectively, yet with a minor additional algorithmic overhead. We compare DSO with several algorithms from the state-of-the-art on two sets of problems, namely: (1) seven well-known constrained engineering design problems and (2) the CEC 2017 benchmark for constrained optimization. Our results show that DSO can achieve state-of-the-art performances, being capable to automatically adjust its behavior to the problem at hand.

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A novel evolution strategy for constrained optimization in engineering design

Cited by 5 publications

References 26 publications

Memetic Viability Evolution for Constrained Optimization

Memetic Viability Evolution for Constrained Optimization

Viability Principles for Constrained Optimization Using a (1+1)-CMA-ES

A co-evolutionary algorithm with adaptive penalty function for constrained optimization

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