A Hybrid GA-PSO Approach Based on Similarity for Various Types of Economic Dispatch Problems

Güvenç, Uğur; Duman, Serhat; Saraçoğlu, Burak Ömer; Öztürk, Ali

doi:10.5755/j01.eee.108.2.155

Cited by 14 publications

(10 citation statements)

References 17 publications

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“…An efficient method based on a hybrid genetic algorithm-particle swarm optimization (GA-PSO) is presented for various types of economic dispatch (ED) problem [40]. The arithmetic crossover operator is used as crossover operator in the genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

An Improved Particle Swarm Optimization Algorithm for Traveling Salesman Problems

Yan¹,

Yang²,

Liang³

et al. 2017

IJCA

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Section: Related Workmentioning

confidence: 99%

An Improved Particle Swarm Optimization Algorithm for Traveling Salesman Problems

Yan¹,

Yang²,

Liang³

et al. 2017

IJCA

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“…The obtained results for the 6-unit system using the hybrid PSO-GSA method are given in Table 3 and the results are compared with other methods reported in literature, including GA, PSO and IDP [21], RGA and GA-PSO [22]. It can be observed that PSO-GSA can get total generation cost of 15,441 ($/hr) and power losses of 12.2417 (MW), which is the best solution among all the methods.…”

Section: Test Case 1: 6-unit Systemmentioning

confidence: 99%

A Novel Hybrid PSO-GSA Method for Non-convex Economic Dispatch Problems

Hardiansyah¹

2013

IJIEEB

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Abstract-This paper proposes a novel and efficient hybrid algorithm based on combining particle swarm optimization (PSO) and gravitational search algorithm (GSA) techniques, called PSO-GSA. The core of this algorithm is to combine the ability of social thinking in PSO with the local search capability of GSA. Many practical constraints of generators, such as power loss, ramp rate limits, prohibited operating zones and valve point effect, are considered. The new algorithm is implemented to the non-convex economic dispatch (ED) problem so as to minimize the total generation cost when considering the linear and non linear constraints. In order to validate of the proposed algorithm, it is applied to two cases with six and thirteen generators, respectively. The results show that the proposed algorithms indeed produce more optimal solution in both cases when compared results of other optimization algorithms reported in literature.

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“…It has been applied on many real-time problems. [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] Its mechanism of solution improvement and information sharing procedure are unique and di®er from that used in the ABC algorithm. It consists of using special parameter called Velocity (v), which is used to control the degree of improvement to be conduct on the solution based on its previous state.…”

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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A Hybrid GA-PSO Approach Based on Similarity for Various Types of Economic Dispatch Problems

Cited by 14 publications

References 17 publications

An Improved Particle Swarm Optimization Algorithm for Traveling Salesman Problems

An Improved Particle Swarm Optimization Algorithm for Traveling Salesman Problems

A Novel Hybrid PSO-GSA Method for Non-convex Economic Dispatch Problems

A hybrid artificial bee colony algorithm for numerical function optimization

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