Improving Performance via Population Growth and Local Search: The Case of the Artificial Bee Colony Algorithm

Aydın, Doğan; Liao, Tianjun; de, Marco A. Montes; Stützle, Thomas

doi:10.1007/978-3-642-35533-2_8

Cited by 21 publications

(18 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the original artificial bee colony algorithm [11], food source position represents a solution and its nectar amount represents fitness value. The employed and onlooker bee In the incremental artificial bee colony algorithm, Food sources are increased with a control parameter "g", that means population size is increased in incremental ABC algorithm.…”

Section: Incremental Abc Algorithm With Local Searchmentioning

confidence: 99%

Incremental Enhanced Artificial Bee Colony Algorithm with Local Search

Pathak¹,

Mishra²,

Kushwah³

2015

IJCA

View full text Add to dashboard Cite

Swarm intelligence is the collective problem solving behavior of groups of artificial agents. These agents local interaction with each other can be negative, positive or neutral. Here positive interaction helps agents to solve a problem while negative interaction block the agents for solving problem and neutral interaction does not affect the swarm"s performance. In this work, incremental enhanced ABC algorithm with local search is used for reducing negative interaction without complexifying the agent"s behavior. Here in the enhanced artificial bee colony algorithm, one additional phase in the form of mutation operator is used. With the help of mutation operator, algorithm may not be trapped into local optima due to the chance of changing local best position.The experimental results show that the performance of proposed algorithm and the proposed algorithm is compared with standard ABC algorithm, Artificial Bee Colony algorithm with mutation algorithm.

show abstract

Section: Incremental Abc Algorithm With Local Searchmentioning

confidence: 99%

Incremental Enhanced Artificial Bee Colony Algorithm with Local Search

Pathak¹,

Mishra²,

Kushwah³

2015

IJCA

View full text Add to dashboard Cite

show abstract

“…In particular, it was found to be relatively poor performing on composite and non-separable function as well as having a slow convergence rate towards high quality solutions [Akay and Karaboga, 2012]. Therefore, in the following years, a number of modifications of the original ABC algorithm were introduced trying to improve performance [Alataş, 2010, Aydın et al, 2012, Banharnsakun et al, 2011, Diwold et al, 2011a, Gao and Liu, 2011, Kang et al, 2011, Zhu and Kwong, 2010. Unfortunately, so far there is no comprehensive comparative evaluation of the performance of ABC variants on a significantly large benchmark set available.…”

Section: Artificial Bee Coloniesmentioning

confidence: 99%

“…Aydın et al [2012] have proposed an algorithm that integrates the incremental social learning (ISL) framework [Montes de Oca, 2011] and local search procedures to ABC. The basic idea of ISL when applied to population-based algorithms for optimization problems is to start the algorithm with a small population size, and to add new agents after each g iterations (implementing the population growth), biased by members of the population (implementing the learning aspect).…”

Section: E22 Variants Of the Artificial Bee Colony Algorithmmentioning

confidence: 99%

“…This modification increases the exploitation behavior of the algorithm but it may increase chances of early convergence. Aydın et al [2012] considered the hybridization of IABC with either Powell's conjugate direction set method [Powell, 1964] or Lin-Yu Tseng's Mtsls1 [Tseng and Chen, 2008] local search procedure resulting into a hybrid IABCLS algorithm. This was done by invoking a local search procedure in every algorithm iteration starting from the best-so-far solution.…”

Section: E22 Variants Of the Artificial Bee Colony Algorithmmentioning

confidence: 99%

“…The reason for selecting these methods is that they are relatively simple to implement but still rather powerful local search algorithms. Additionally, in some of our previous research, we were able to design with these two local searches some very high performing continuous optimization algorithms [Aydın et al, 2012, Liao et al, 2011b, making them also a natural choice for this task here. In fact, from our work on IABC, we already knew that both local search algorithms may contribute positively to performance.…”

Section: E32 Local Searchmentioning

confidence: 99%

See 2 more Smart Citations

Population-based heuristic algorithms for continuous and mixed discrete-continuous optimization problems

Liao¹

2015

4OR-Q J Oper Res

Self Cite

View full text Add to dashboard Cite

ii SummaryContinuous optimization problems are optimization problems where all variables have a domain that typically is a subset of the real numbers; mixed discretecontinuous optimization problems have additionally other types of variables, so that some variables are continuous and others are on an ordinal or categorical scale. Continuous and mixed discrete-continuous problems have a wide range of applications in disciplines such as computer science, mechanical or electrical engineering, economics and bioinformatics. These problems are also often hard to solve due to their inherent difficulties such as a large number of variables, many local optima or other factors making problems hard. Therefore, in this thesis our focus is on the design, engineering and configuration of high-performing heuristic optimization algorithms.We tackle continuous and mixed discrete-continuous optimization problems with two classes of population-based heuristic algorithms, ant colony optimization (ACO) algorithms and evolution strategies. In a nutshell, the main contributions of this thesis are that (i) we advance the design and engineering of ACO algorithms to algorithms that are competitive or superior to recent state-of-the-art algorithms for continuous and mixed discrete-continuous optimization problems, (ii) we improve upon a specific state-of-the-art evolution strategy, the covariance matrix adaptation evolution strategy (CMA-ES), and (iii) we extend CMA-ES to tackle mixed discrete-continuous optimization problems.More in detail, we propose a unified ant colony optimization (ACO) framework for continuous optimization (UACOR). This framework synthesizes algorithmic components of two ACO algorithms that have been proposed in the literature and an incremental ACO algorithm with local search for continuous optimization, which we have proposed during my doctoral research. The design of UACOR allows the usage of automatic algorithm configuration techniques to automatically derive new, high-performing ACO algorithms for continuous optimization. We also propose iCMAES-ILS, a hybrid algorithm that loosely couples IPOP-CMA-ES, a CMA-ES variant that uses a restart schema coupled with an increasing population iii size, and a new iterated local search (ILS) algorithm for continuous optimization. The hybrid algorithm consists of an initial competition phase, in which IPOP-CMA-ES and the ILS algorithm compete for further deployment during a second phase. A cooperative aspect of the hybrid algorithm is implemented in the form of some limited information exchange from IPOP-CMA-ES to the ILS algorithm during the initial phase. Experimental studies on recent benchmark functions suites show that UACOR and iCMAES-ILS are competitive or superior to other state-of-the-art algorithms.To tackle mixed discrete-continuous optimization problems, we extend ACO MV and propose CES MV , an ant colony optimization algorithm and a covariance matrix adaptation evolution strategy, respectively. In ACO MV and CES MV , the decision variables of an optimization prob...

show abstract

An innovative hybrid algorithm to solve nonconvex economic load dispatch problem with or without valve point effects

Parouha

Verma²

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Improving Performance via Population Growth and Local Search: The Case of the Artificial Bee Colony Algorithm

Cited by 21 publications

References 14 publications

Incremental Enhanced Artificial Bee Colony Algorithm with Local Search

Incremental Enhanced Artificial Bee Colony Algorithm with Local Search

Population-based heuristic algorithms for continuous and mixed discrete-continuous optimization problems

An innovative hybrid algorithm to solve nonconvex economic load dispatch problem with or without valve point effects

Contact Info

Product

Resources

About