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Bees algorithm for generalized assignment problem
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Home > Books > Swarm Intelligence, Focus on Ant and Particle Swarm Optimization
Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem
Published: 01 December 2007
DOI: 10.5772/5101
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Artificial bee colony algorithm with global and local neighborhoods
- Original Article
- Published: 13 August 2014
- Volume 9 , pages 589–601, ( 2018 )
Cite this article
- Shimpi Singh Jadon 1 ,
- Jagdish Chand Bansal 2 ,
- Ritu Tiwari 1 &
- Harish Sharma 3
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Artificial Bee Colony (ABC) is a well known population based efficient algorithm for global optimization. Though, ABC is a competitive algorithm as compared to many other optimization techniques, the drawbacks like preference on exploration at the cost of exploitation and slow convergence are also associated with it. In this article, basic ABC algorithm is studied by modifying its position update equation using the differential evolution with global and local neighborhoods like concept of food sources’ neighborhoods. Neighborhood of each colony member includes \(10\,\%\) members from the whole colony based on the index-graph of solution vectors. The proposed ABC is named as ABC with Global and Local Neighborhoods (ABCGLN) which concentrates to set a trade off between the exploration and exploitation and therefore increases the convergence rate of ABC. To validate the performance of proposed algorithm, ABCGLN is tested over \(24\) benchmark optimization functions and compared with standard ABC as well as its recent popular variants namely, Gbest guided ABC, Best-So-Far ABC and Modified ABC. Intensive statistical analyses of the results shows that ABCGLN is significantly better and takes on an average half number of function evaluations as compared to other considered algorithms.
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Jadon, S.S., Bansal, J.C., Tiwari, R. et al. Artificial bee colony algorithm with global and local neighborhoods. Int J Syst Assur Eng Manag 9 , 589–601 (2018). https://doi.org/10.1007/s13198-014-0286-6
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DOI : https://doi.org/10.1007/s13198-014-0286-6
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The proposed bee algorithm is a modified version of the basic "bees algorithm" that was initially proposed by Pham and his colleagues. The modified BA is found very effective in solving small to medium sized generalized assignment problems. Actually, the proposed algorithm easily found all of the optimal solutions for smaller size problems ...
Bees algorithm (BA) is a new member of meta-heuristics. BA tries to model natural behavior of honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new algorithms for solving optimization problems.
Abstract. Bees algorithm (BA) is a new member of meta-heuristics. BA tries to model natural behavior of honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new algorithms for solving optimization problems.
In this chapter an extensive review of work on artificial bee algorithms is given, development of an ABC algorithm for solving generalized assignment problem which is known as NP-hard problem is presented in detail along with some comparisons. Expand. 260. Highly Influential.
The generalized assignment problem (GAP) is an open problem in which an integer k is given and one wants to assign k′ agents to kk′≤k jobs such that the sum of the corresponding cost is minimal.
Such an attempt is made in this paper to present the performance of bee inspired algorithm, BA on a NP-hard problem which is known as generalized assignment problem, GAP. The proposed bee algorithm is a modified version of the basic ''bees algorithm" that was initially proposed by Pham and his colleagues.
Table 10 Comparison of results for gapa-gapd. - "Bees algorithm for generalized assignment problem" ... "Bees algorithm for generalized assignment problem" Skip to search form Skip to main content Skip to account menu. Semantic Scholar's Logo. Search 213,686,415 papers from all fields of science.
8 Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem Adil Baykaso ù lu 1, Lale Özbak r 2 and P nar Tapkan 2 1University of Gaziantep, Department of Industrial Engineering 2Erciyes University, Department of Industrial Engineering Turkey 1. Introduction There is a trend in the scientific community to model and solve complex optimization
A Path Relinking Algorithm for the Generalized Assignment Problem, In M.G.C. Resende, J.P. de Sousa (Eds.), Metaheuristics: Computer Decision-Making, Kluwer Academic Publishers, Boston, 1-17,2004 ...
Fig. 1. Typical behavior of honey bee foraging [27]. - "Bees algorithm for generalized assignment problem"
The generalized assignment problem is NP-hard, However, there are linear-programming relaxations which give a (/)-approximation. Greedy approximation algorithm. For the problem variant in which not every item must be assigned to a bin, there is a family of algorithms for solving the GAP by using a combinatorial translation of any algorithm for ...
We consider the generalized assignment problem in which the objective is to find a minimum cost assignment of a set of jobs to a set of agents subject to resource constraints. The presented new approach is based on a previously published, successful hybrid genetic algorithm and includes as new features two alternative initialization heuristics ...
Modified version of ABC algorithm is introduced for optimization problems and standardized and reusable module to solve generalized assignment problem is developed. The Scrounging-Act Scheme of Bees is taken in to consideration for modeling, which reflects the population characteristics of Bees. The honey bees' natural conduct in food scrounging-Act is what the model tries to contrive. The ...
Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem. Written By. Adil Baykasoğlu, Lale Özbakır and Pınar Tapkan. Published: 01 December 2007. DOI: 10.5772/5101. IntechOpen. Swarm Intelligence Focus on Ant and Particle Swarm Optimization Edited by Felix Chan. From the Edited Volume.
A survey of the generalized assignment problem and its applications. Information Systems and Operational Research. v45 i3. 123-141. Google Scholar; Özbakır et al., 2010. Bees algorithm for generalized assignment problem. Applied Mathematics and Computation. v215. 3782-3795. Google Scholar; Pham et al., 2006.
Baykasoglu A, Ozbakir L, Tapkan P (2007) Artificial bee colony algorithm and its application to generalized assignment problem. Swarm Intell 113-144. Chidambaram C, Lopes HS (2009) A new approach for template matching in digital images using an artificial bee colony algorithm. In Nature & Biologically Inspired Computing, 2009. NaBIC 2009.
In this chapter an extensive review of work on artificial bee algorithms is given, development of an ABC algorithm for solving generalized assignment problem which is known as NP-hard problem is presented in detail along with some comparisons. There is a trend in the scientific community to model and solve complex optimization problems by employing natural metaphors.
Abstract. The generalized assignment problem (GAP) is an open problem in which an integer is given and one wants to assign agents to jobs such that the sum of the corresponding cost is minimal. Unlike the traditional -cardinality assignment problem, a job can be assigned to many, but different, agents and an agent may undertake several, but different, jobs in our problem.
A new algorithm to solve the uncertain generalized assignment problem is studied, based on the concept of branch and bound rather than the usual simplex based techniques, which is justified numerically by showing its application in generalized machine allocation problem.
How to solve a (generalized) assignment problem? i.e. we have clients from a certain industry and with a certain value assigned to an advisor. The goal is to harmonize the Client - Advisor assignment in a way that the number of industries per advisor gets minimized while preserving as many of the current clients as possible (e.g. "AD1" has most ...