A novel global optimization technique for high dimensional functions

Groşan, Crina; Abraham, Ajith

doi:10.1002/int.20343

Cited by 26 publications

(29 citation statements)

References 30 publications

(8 reference statements)

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“…Genetic algorithm(GA) has been proposed in [4,5], [7],particle swarm optimization algorithm proposed in [8], and the non-evolutionary approaches like simulated annealing in [9], tabusearch [10], ant-colony optimization algorithm proposed in [11], and artificial bee colony algorithm in [12]. The combination of the approaches with swarm intelligence methods is widely used to resolve different practical problems of engineering and science [13][14][15][16][17][18]. Self-adaptive differential evolution as been proposed in [24,25].…”

Section: Related Workmentioning

confidence: 99%

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Hypercube optimization based global solution in numerical benchmark and sensor localization

R.Sujatha¹,

Siddappa²

2018

IJET

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An original learning algorithm for solving global numerical optimization problems is proposed. The proposed algorithm is strong stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The hypercube optimization algorithm includes the initialization and evaluation process, and searching space process. The designed HO algorithm is tested on specific benchmark functions. The comparative performance analysis have made against with other approaches of dynamic weight particle swarm optimization and self-adaptive differential evolution algorithm. Convergence characteristics of self-adaptive differential evolution algorithm has deliver the much better functional value in compare to dynamic weight based particle swarm optimization.

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Section: Related Workmentioning

confidence: 99%

“…The proposed HO algorithm is verified on three continues test functions which are widely used in all applications like Rosenbrock function, Rastrigin function, Ackley path function [18][19][20][21][22]. The test functions are more applicable for the experimental evaluations of methods used in global optimization problems.…”

Section: Test Functionsmentioning

confidence: 99%

Hypercube optimization based global solution in numerical benchmark and sensor localization

R.Sujatha¹,

Siddappa²

2018

IJET

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“…The line search [13] is a standard and well established optimization technique [14,15]. The standard line search technique is modified in this paper so that it is able to generate the set of nondominated solutions for a MOP.…”

Section: Line Search Generator Of Pareto Frontiermentioning

confidence: 99%

Approximating Pareto frontier using a hybrid line search approach

Groşan

Abraham

2010

Information Sciences

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a b s t r a c tThe aggregation of objectives in multiple criteria programming is one of the simplest and widely used approach. But it is well known that this technique sometimes fail in different aspects for determining the Pareto frontier. This paper proposes a new approach for multicriteria optimization, which aggregates the objective functions and uses a line search method in order to locate an approximate efficient point. Once the first Pareto solution is obtained, a simplified version of the former one is used in the context of Pareto dominance to obtain a set of efficient points, which will assure a thorough distribution of solutions on the Pareto frontier. In the current form, the proposed technique is well suitable for problems having multiple objectives (it is not limited to bi-objective problems) and require the functions to be continuous twice differentiable. In order to assess the effectiveness of this approach, some experiments were performed and compared with two recent well known population-based metaheuristics namely ParEGO and NSGA II. When compared to ParEGO and NSGA II, the proposed approach not only assures a better convergence to the Pareto frontier but also illustrates a good distribution of solutions. From a computational point of view, both stages of the line search converge within a short time (average about 150 ms for the first stage and about 20 ms for the second stage). Apart from this, the proposed technique is very simple, easy to implement and use to solve multiobjective problems.

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“…High-dimensional opti-6 mization problems can be formulated as a D-dimensional minimiza-7 tion problem as follows (Gergel, 1997 high-dimensional problems is a considerable challenge. 16 Due to the practical demands, there were some attempts in try-17 ing to use different methods for high-dimensional optimization prob-18 lems in recent years (Grosan & Abraham, 2009). One method is to use 19 parallel optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%