Improved artificial bee colony algorithm for global optimization

Gao, Weifeng; Liu, Sanyang

doi:10.1016/j.ipl.2011.06.002

Cited by 306 publications

(161 citation statements)

References 25 publications

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“…The simulation result is shown in Figure 3: In order to verify the advantage of cooperative network model, some simulations were taken based on different algorithms. First of all, simulations of core monitoring area were conducted to obtain the relations between coverage and the number of sensor nodes by using 25 nodes and different algorithms for simulation, including genetic algorithms [22], PSO [23], fish algorithm [24], artificial bee colony algorithm [25]and used virtual force algorithm of this model. From the Table 1, We can get that the virtual force algorithm can achieve 100% coverage.…”

Section: Simulation and Discussionmentioning

confidence: 99%

Method of Wireless Sensor Network Node Deployment for the Emergency Environment

Ge¹,

Wang²,

Hu³

et al. 2017

Int. J. Onl. Eng.

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Abstract-Incidents could not always be avoided, resulting in significant losses for the state property and people's safety. Effective monitoring of incidents is becoming more and more important. Wireless sensor networks (WSNs) are widely used in environmental monitoring. On the basis of the existing wireless sensor network node deployment model, a network node deployment model was proposed, which was used in the monitoring of unexpected accidents. The model area was divided into the core area and the evacuation area, and different monitoring nodes deployment plans were applied in different areas. The simulation experiment is carried out by MATLAB, and the simulation results of the node coverage are obtained. Results show that the model for the practical application of the node deployment provides an effective deployment plan, as well as offers a strong basis for real-time monitoring and post-accident emergency evacuation.

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Section: Simulation and Discussionmentioning

confidence: 99%

Method of Wireless Sensor Network Node Deployment for the Emergency Environment

Ge¹,

Wang²,

Hu³

et al. 2017

Int. J. Onl. Eng.

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“…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%

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

Liao¹

2015

4OR-Q J Oper Res

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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...

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“…Thus, the results show that the ABC is very efficient in solving the data clustering problems. S.Liu.et.al [17] proposes an Improved ABC (IABC) algorithm for global optimization. The IABC proposes improved search equations such as ABC/best/1 and ABC/rand/1.…”

Section: International Journal Of Computer Applications (0975 -8887)mentioning

confidence: 99%

A Survey on Optimization Algorithms for Optimizing the Numerical Functions

George¹,

Raimond²

2013

IJCA

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Till now many optimization algorithms are available for solving complex problems. The algorithm that has created an impact is the numerical function optimization. Some of the existing optimization algorithms are classified into evolutionary computing and metaheuristic methods. A comprehensive survey on numerical function optimization is presented below with a deeper view into different aspects of the algorithms.

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Improved artificial bee colony algorithm for global optimization

Cited by 306 publications

References 25 publications

Method of Wireless Sensor Network Node Deployment for the Emergency Environment

Method of Wireless Sensor Network Node Deployment for the Emergency Environment

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

A Survey on Optimization Algorithms for Optimizing the Numerical Functions

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