Autonomous Bee Colony Optimization for multi-objective function

Zeng, Fanchao; Decraene, James; Low, Malcolm Yoke Hean; Hingston, Philip; Cai, Wentong; Zhou, Shan; Chandramohan, Mahinthan

doi:10.1109/cec.2010.5586057

Cited by 15 publications

(7 citation statements)

References 17 publications

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“…Clearly, regarding the HV (Fonseca and Flemming 1998;Zitzler et al 2007) indicator, the higher the value (i.e., the greater the hypervolume), the better is the computed front. HV is able to capture in a single number both the closeness of the solutions to the optimal set and, to a certain degree, the spread of the solutions across the objective space (Zeng et al 2010). The second indicator, the IGD (Zitzler et al 2003), measures how far the elements are in the Pareto optimal set from those in the set of non-dominated vectors found.…”

Section: Deb-theile-laumanns-zitzler's Function N5 (Dtlz5) Problemmentioning

confidence: 99%

An experimental analysis of a new two-stage crossover operator for multiobjective optimization

Λιαγκούρας

Metaxiotis

2015

Soft Comput

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Evolutionary algorithms for multiobjective problems utilize three types of operations for progressing toward the higher fitness regions of the search space. Each type of operator contributes in a different way toward the achievement of the common goal. The mutation operation is responsible for diversity maintenance, while the selection operation favors the survival of the fittest. In this paper we focus our attention on the crossover operator. The crossover operator by default is responsible for the search effort and as such deserves our special attention. In particular, we propose a two-stage crossover (TSX) operator for more efficient exploration of the search space. The performance of the proposed TSX operator is assessed in comparison with the simulated binary crossover operator with the assistance of three wellknown multiobjective evolutionary algorithms, namely the NSGAII, the SPEA2 and the MOCELL, for the solution of the DTLZ1-7 set of test functions. We also compare the proposed TSX with other popular reproduction operators like the differential evolution and the particle swarm optimization. Finally, we examine the efficacy of the TSX operator in handling problems having five objectives. It is shown with the assistance of the Deb, Thiele, Laumanns and Zitzler set of test functions that the TSX operator can substantially improve the results generated by three popular performance metrics for most of the cases.

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Section: Deb-theile-laumanns-zitzler's Function N5 (Dtlz5) Problemmentioning

confidence: 99%

An experimental analysis of a new two-stage crossover operator for multiobjective optimization

Λιαγκούρας

Metaxiotis

2015

Soft Comput

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“…In the proposed method the involvement of a scout bee in each iteration of back-propagation optimization phase is decided in accordance with the average performance of solutions and thus results in the maintenance of the process of exploitation. Fanchao Z., James D. [16] have used an Autonomous Bee Colony Optimization (A-BCO) algorithm for solving multi-objective numerical problems for optimizing the balance between exploration and exploitation during the search process for solving the multi-objective numerical problems. Its advantage is that the self-organized and collective behavior exhibited by the colony insects enables them to solve multi-objective problems which cannot be addressed by single insects by acting independently.…”

Section: Introductionmentioning

confidence: 99%

A literature review of Bee Colony optimization algorithms

Gulati¹,

Vats²

2014

2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls With Their Impact on Humanity (CIPECH)

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Bee Colony optimization techniques are inspired by the high level of mutual intelligence shown by the natural bees in the food foraging process. It is a population based natural search algorithm which provides the base to solve metaheuristic computational optimization problems. In this paper we have carried out a literature review of the applications of BCO into various areas of computational problems where they prove their worth in providing optimized solutions. We have further carried out a tabular comparison of the work performed by the various researchers by applying the the BCO as optimization algorithms for solving the Optimization Problems.

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“… Multi-objective Particle Swarm optimization (MOPSO) (Coello et al, 2004)  PSO-based multi-objective optimization with dynamic population size and adaptive local archives (Leong & Yen, 2008)  Covering Pareto-optimal fronts by sub swarms in multi-objective particle swarm optimization (Mostaghim & Teich, 2004)  Particle swarm inspired evolutionary algorithm (PS-EA) for multi-objective optimization problem (Srinivasan & Seow, 2003)  Interactive Particle Swarm Optimization (IPSO) (Agrawal et al, 2008)  Dynamic Multiple Swarms in Multi-Objective Particle Swarm Optimization (DSMOPSO) (Yen & Leong, 2009)  Autonomous bee colony optimization for multi-objective function (Zeng et al, 2010)  A multi-objective artificial bee colony for optimizing multi-objective problems (Hedayatzadeh et al, 2010)  A novel multi-objective optimization algorithm based on artificial bee colony (Zou et al, 2011)  Multi-objective bee swarm optimization (Akbari & Ziarati, 2012)  Multi-objective artificial bee colony algorithm (Akbari & Ziarati, 2012) The evolutionary and swarm intelligence based algorithms are probabilistic algorithms and required common controlling parameters like population size and number of generations. Besides the common control parameters, different algorithms require their own algorithm-specific control parameters.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

Rao¹,

Patel²

2013

10.5267/j.ijiec

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The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO) algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front) maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009) competition. The performance assessment is done by using the inverted generational distance (IGD) measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

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Autonomous Bee Colony Optimization for multi-objective function

Cited by 15 publications

References 17 publications

An experimental analysis of a new two-stage crossover operator for multiobjective optimization

An experimental analysis of a new two-stage crossover operator for multiobjective optimization

A literature review of Bee Colony optimization algorithms

A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

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