An Imperialistic Strategy Approach to Continuous Global Optimization Problem

Anescu, George

doi:10.1109/synasc.2014.79

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…In order to conduct the tests, an appropriate testing methodology was devised (see also [10], [11], [12]). When the quality of an optimization method is estimated, two (often conflicting) characteristics are of interest: a small number of function evaluations (N F E) and a high success rate (SR).…”

Section: Testing and Resultsmentioning

confidence: 99%

A Heuristic Fast Gradient Descent Method for Unimodal Optimization

Anescu

2018

JAMCS

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The known gradient descent optimization methods applied to convex functions are using the gradient's magnitude in order to adaptively determine the current step size. The paper is presenting a new heuristic fast gradient descent (HFGD) approach, which uses the change in gradient's direction in order to adaptively determine the current step size. The new approach can be applied to solve classes of unimodal functions more general than the convex functions (e.g., quasi-convex functions), or as a local optimization method in multimodal optimization. Testing conducted on a testbed of 16 test functions showed an overall much better efficiency and an overall better success rate of the proposed HFGD method when compared to other three known first order gradient descent methods.

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Section: Testing and Resultsmentioning

confidence: 99%

A Heuristic Fast Gradient Descent Method for Unimodal Optimization

Anescu

2018

JAMCS

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“…In order to conduct the tests an appropriate testing methodology was devised (see also [16], [17]). When the quality of an optimization method is estimated, two often conflicting characteristics are of interest: a small number of function evaluations, N F E, and a high success rate, SR. For test functions with known solutions the success can be simply defined as the achievement of an absolute or relative precision tolerance to the known solutions.…”

Section: Testing Methodologymentioning

confidence: 99%

Gradual and Cumulative Improvements to the Classical Differential Evolution Scheme through Experiments

Anescu

2016

Annals of West University of Timisoara - Mathematics and Computer Science

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Abstract. The paper presents the experimental results of some tests conducted with the purpose to gradually and cumulatively improve the classical DE scheme in both efficiency and success rate. The modifications consisted in the randomization of the scaling factor (a simple jitter scheme), a more efficient Random Greedy Selection scheme, an adaptive scheme for the crossover probability and a resetting mechanism for the agents. After each modification step, experiments have been conducted on a set of 11 scalable, multimodal, continuous optimization functions in order to analyze the improvements and decide the new improvement direction. Finally, only the initial classical scheme and the constructed Fast Self-Adaptive DE (FSA-DE ) variant were compared with the purpose of testing their performance degradation with the increase of the search space dimension. The experimental results demonstrated the superiority of the proposed FSA-DE variant.

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NSC-PSO, a Novel PSO Variant without Speeds and Coefficients

Anescu

Prisecaru

2015

2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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An Imperialistic Strategy Approach to Continuous Global Optimization Problem

Cited by 3 publications

References 13 publications

A Heuristic Fast Gradient Descent Method for Unimodal Optimization

A Heuristic Fast Gradient Descent Method for Unimodal Optimization

Gradual and Cumulative Improvements to the Classical Differential Evolution Scheme through Experiments

NSC-PSO, a Novel PSO Variant without Speeds and Coefficients

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