Levy mutated Artificial Bee Colony algorithm for global optimization

Anguluri, Rajasekhar; Abraham, Ajith; Pant, Millie

doi:10.1109/icsmc.2011.6083786

Cited by 29 publications

(17 citation statements)

References 13 publications

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“…Artificial Bee Colony (ABC) algorithm was first proposed and developed by Karaboga [12,13] for solving numerical optimization problems [14]. Since its inception in 2005, ABC has been evolved in to a very powerful optimization tool in the field of swarm intelligence.…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

Cooperative Micro Artificial Bee Colony Algorithm for Large Scale Global Optimization Problems

Anguluri

Das

2013

Lecture Notes in Computer Science

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Abstract. Large scale optimization problems or optimization problems involving high-dimensions often appear in real world application scenario. The mathematical representation of these problems appears similar to that of traditional low dimensional problems but they exhibit high interdependencies among the variables to be optimized. Hence normal evolutionary algorithms or swarm intelligence based methods cannot be directly operated on these problems to find global optimum. In these situations, cooperating approaches are proved to be very valuable, since they are based on though simple yet power strategy "divide and conquer". Though handy, computational burden of cooperative approach oriented methods will be high, as they involve optimization of various subcomponents for predefined number of steps. On other hand, recently evolved Micro Evolutionary Algorithms (micro-EAs) are shown to be very powerful strategies for solving optimization problems, as they involve very small population of just a few individuals. This advantage of micro-EA is accompanied by its tendency towards to get stuck in local optima. Hence this paper tries to combine the advantages of both cooperative strategies and also micro-EAs nature accompanied with a swarm intelligent Artificial Bee Colony (ABC) algorithm as main optimizer, to solve optimization problems of very high dimension. The proposed variant is termed as "Cooperative Micro-Artificial Bee Colony" (CMABC) algorithm. Computer simulations over benchmark suite considered and also extensive comparisons over cooperative variants of state-of-art Differential Evolution method show the superiority of proposed algorithm.

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Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

Cooperative Micro Artificial Bee Colony Algorithm for Large Scale Global Optimization Problems

Anguluri

Das

2013

Lecture Notes in Computer Science

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“…The first method is based on a non-linear interpolated path while in the second one, scout bees follow a Gaussian movement. Rajasekhar et al [2011] proposed an improved version of ABC algorithm with mutation based on Levy probability distributions. Global ABC algorihm, which was introduced by Guo et al [2011], proposed three modified search equation by using global and individual best positions.…”

Section: E22 Variants Of the Artificial Bee Colony Algorithmmentioning

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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“…This modified version was suggested by Rajasekhar et al [14] and gave good performance in solving standard benchmark problems for different dimensions. Artificial Bee Colony (ABC) is one of the most recent, swarm intelligence based algorithms employing the foraging behavior of honey bees for solving dynamic numerical optimization problems.…”

Section: Introductionmentioning

confidence: 99%

“…However, the performance of an algorithm depends on both exploration and exploitation phases for a required feasible solution. Various modifications have been suggested in literature to improve performance of ABC [21,22].Motivated by the performance of L-ABC on various benchmark problems of higher dimension [14], in this present study we applied L-ABC to analyze the performance of FOPID controller in DC motor. This paper is organized as follows: In section II, we present a mathematical model of DC motor as a controlled object.…”

Section: Introductionmentioning

confidence: 99%

Fractinal order speed control of DC motor using levy mutated Artificial Bee Colony Algorithm

Anguluri

Kunathi

Abraham

et al. 2011

2011 World Congress on Information and Communication Technologies

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Abstract-In this paper we propose a new technique for designing feedback control of a DC motor speed using fractional order proportional-integral-derivative (FOPID controller). DC motor is often used in robotics and other fields of control and therefore speed control is very important. FOPID controller's parameters are composed of the proportionality constant, integral constant, derivative constant, derivative order and integral order, and its design is more complex than that of conventional integer-order proportionalintegral-derivative (PID) controllers. Here the controller synthesis is formulated as a single objective optimization problem and based on Integral Time Absolute Error (ITAE) criterion. A modified Artificial Bee Colony Algorithm is been used to tune the FOPID controller parameters.

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Levy mutated Artificial Bee Colony algorithm for global optimization

Cited by 29 publications

References 13 publications

Cooperative Micro Artificial Bee Colony Algorithm for Large Scale Global Optimization Problems

Cooperative Micro Artificial Bee Colony Algorithm for Large Scale Global Optimization Problems

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

Fractinal order speed control of DC motor using levy mutated Artificial Bee Colony Algorithm

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