A modified artificial bee colony based on chaos theory for solving non-convex emission/economic dispatch

Shayeghi, Hossein; Ghasemi, Ali

doi:10.1016/j.enconman.2013.12.028

Cited by 66 publications

(34 citation statements)

References 38 publications

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“…paper which will be solved using the proposed Quasi-Oppositional Artificial Bee Colony (QOABC) algorithm. In fact, to cope with the disadvantage of traditional algorithms to find global optimum solution, and to guarantee the solutions are not converged in local optima when system has too many optimization variables [24], an innovative ABC algorithm is suggested using quasi opposition-based learning [25], called QOABC to be able to optimize the parameters of the MIMO-LSSVM model.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous day-ahead forecasting of electricity price and load in smart grids

Shayeghi

Ghasemi

Moradzadeh

et al. 2015

Energy Conversion and Management

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Section: Introductionmentioning

confidence: 99%

Simultaneous day-ahead forecasting of electricity price and load in smart grids

Shayeghi

Ghasemi

Moradzadeh

et al. 2015

Energy Conversion and Management

Self Cite

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“…9,17,[19][20][21]23,24,26,28,29,[46][47][48] In this section, a set of experiments tested on the six numerical benchmark functions were performed to compare the performance of the HPABC with that of other algorithms. In order to analytically investigate the new proposed HPABC algorithm, two algorithms the original ABC and the original PSO algorithms will be used for the comparison.…”

Section: Abc and Pso Parameters Settingsmentioning

confidence: 99%

“…22 In order to address the disadvantages, several variants of ABC have been proposed to enhance the exploration and exploitation capability, convergence speed and avoid being trapped at the local optimum. [16][17][18]20,[23][24][25][26][27][28][29] The modi¯cations include changes on the ABC itself or hybridizing it with other optimization algorithms. Reference 29 was among the recent works involving the modi¯cation of the ABC algorithm, where a selection mechanism is added after the employed bee phase so that only a number of solutions will be stored in a separate memory for the next phase.…”

Section: Introductionmentioning

confidence: 99%

A hybrid artificial bee colony algorithm for numerical function optimization

Alqattan

Abdullah

2015

Int. J. Mod. Phys. C

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Arti¯cial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

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“…In 2005, artificial bee colony algorithm (Artificial Bee Colony, ABC) [1][2][3], which is based on self-organization model [4][5], has been proposed. Its application is mainly used in the field of numerical optimization [6][7][8][9][10][11][12][13]. Thus, domestic and foreign researchers verify the outstanding performance in function optimization domain according to the comparison to other algorithms with a variety improvement of the algorithm.…”

Section: Introductionmentioning

confidence: 99%