A new unconstrained global optimization method based on clustering and parabolic approximation

Pençe, İhsan; Çeşmeli, Melike Şişeci; Şenel, Fatih Ahmet; Çetişli, Bayram

doi:10.1016/j.eswa.2016.02.036

Cited by 15 publications

(9 citation statements)

References 30 publications

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“…In each step, the data are clustered and the cluster centers denote the local optimums. These centers are adapted with locally fitted parabolas [10]. GOBC-PA is superior to other stochastic algorithms in terms of speed.…”

Section: Gobc-pamentioning

confidence: 99%

“…Curve fitting process to the clusters is performed using second order polynomials and can be seen in Figure 9. The vertex points of polynomials can stay on maxima or minima of the objective function [10].…”

Section: Gobc-pamentioning

confidence: 99%

“…Curve fitting process to the clusters on GOBC-PA [10] In this study, GOBC-PA method's objective function is determined as epsilon value of the gradient that gives the location of basin region. So, only purpose of the stochastic method is to find basin region, not to find global optimum.…”

Section: Figurementioning

confidence: 99%

“…The typical examples of deterministic methods are the filled function method (FFM) [2], trajectory method [3], tunneling method [4] and covering method [5]. The best known algorithms of stochastic methods are the simulated annealing method [6], genetic algorithm (GA) [7], artificial bee colony algorithm (ABC) [8], particle swarm optimization (PSO) [9] and unconstrained global optimization method based on clustering and parabolic approximation (GOBC-PA) [10].…”

Section: Introductionmentioning

confidence: 99%

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Filled Fonksiyonunda Yeni Bir Stokastik Arama Yöntemi

Pençe

Çeşmeli

2020

El-Cezeri Fen Ve Mühendislik Dergisi

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Deterministic methods are used for optimum solution of many engineering and scientific problems. Filled function method, which is a deterministic method, is not trapped to local minimums with its ability to bypass energy barriers. In order to achieve this, the basin regions of the filled function should be located, and the filled function should be constructed in that region. However, classical search strategies used for finding the basin regions don't yield effective results. In this study, a new stochastic search approach is presented as a faster and more efficient alternative to classic filled function search strategy. An unconstrained global optimization method based on clustering and parabolic approximation (GOBC-PA) has been used as a stochastic method for accelerating the L type filled function as a deterministic method. The developed method has been tested against classical filled function using 11 benchmark functions. When the obtained results are examined, it is seen that the stochastic search approach has superiority over the mean error, standard deviation and elapsed time values according to the classical approach. These results show that the combination of deterministic and stochastic methods can be more successful in finding the global minimum against the classic deterministic method.

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Section: Gobc-pamentioning

confidence: 99%

Section: Gobc-pamentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Filled Fonksiyonunda Yeni Bir Stokastik Arama Yöntemi

Pençe

Çeşmeli

2020

El-Cezeri Fen Ve Mühendislik Dergisi

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“…Metaheuristic algorithms are the non-gradient based algorithms which do not require any continuous objective function and/or its gradients. These methods are generally put forward a simple stochastic mathematic algorithm which is inspired from the natural phenomenon like physical and natural rules, social behaviors or collaboration in the fish and/or birds colonies [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. In addition of these specifications their ease to implementation makes them applicable in solving a wide range of optimization problems [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Effect of fuzzy-membership function types on the search capability of interactive search algorithm

Mortazavi¹

2019

JSEAM

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Fuzzy logic is widely used as the fundamental platform for the many artificial intelligence-based systems like control of subways, air conditioners, transmission systems, facial pattern recognition, antiskid braking systems. In the theory of fuzzy set, membership functions (MFs) provide an infrastructure for determining the degree of truth in a fuzzy model. So, they can play important role in the performance of these logical mechanisms. Different types of MF can be defined to fuzzification-defuzzification process (i.e. converting Boolean input to a fuzzy output and vise-versa). They are categorized and named based on the shape of their diagrams as triangular, Gaussian, trapezoidal and so forth. In the current study the effect of MF's type on the search capability of Interactive Search Algorithm (ISA) is assessed. To meet this aim, a metaheuristic technique that are known as Interactive Search Algorithm and strengthen with fuzzy adjustment mechanism is tested on solving a number of benchmark problems involving different types of MFs. The results indicate that due to stochastic essence of metaheuristic approaches the type of MF does not seriously affect the search capability of this technique. Therefore, it can be stated that the MFs should be selected based on the simplicity criterion. In other words, the simplest MF which provides the designers requirements can be the best choice.

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A Hybrid Optimization Algorithm for Electric Motor Design

Essaid

Idoumghar

Lepagnot

et al. 2018

Lecture Notes in Computer Science

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This paper presents a hybrid algorithm employed to reduce the weight of an electric motor, designed for electric vehicle (EV) propulsion. The approach uses a hybridization between Cuckoo Search and CMAES to generate an initial population. Then, the population is transferred to a new procedure which adaptively switches between two search strategies, i.e. one for exploration and one for exploitation. Besides the electric motor optimization, the proposed algorithm performance is also evaluated using the 15 functions of the CEC 2015 competition benchmark. The results reveal that the proposed approach can show a very competitive performance when compared with different state-of-the-art algorithms.

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A new unconstrained global optimization method based on clustering and parabolic approximation

Cited by 15 publications

References 30 publications

Filled Fonksiyonunda Yeni Bir Stokastik Arama Yöntemi

Filled Fonksiyonunda Yeni Bir Stokastik Arama Yöntemi

Effect of fuzzy-membership function types on the search capability of interactive search algorithm

A Hybrid Optimization Algorithm for Electric Motor Design

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