Artificial bee colony algorithm on distributed environments

Artificial bee colony (ABC) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

Artificial bee colony (ABC) algorithm is inspired by the foraging behavior of the honey bee swarm. It has achieved comparable performance to other population-based optimization algorithms. However, the learning mechanism in ABC algorithm is not well balance between exploration and exploitation. In this paper, a comprehensive learning (CL) re-initialization strategy is introduced into original ABC algorithm to enhance the exploration while the best solution of the bee population is used to enhance the exploitation. The modified ABC with CL reinitialization strategy is tested with CEC 2014 benchmark problems and carried out a comparative study with other ABC algorithms and recent state-of-art algorithm. The results show that the proposed ABC-CL algorithm outperforms compared state-of-art algorithms.

Artificial bee colony (ABC) algorithm is one of the most recently introduced swarm intelligence based algorithm which foraging the behavior of honey bee colonies. In order to improve the convergence performance and searching speed of finding best solution, self adaptive hybrid ABC (SAHABC) is proposed in this paper. For evaluating the performance of standard ABC and proposed SAHABC algorithms, we implemented experiments on CEC 2013 real-parameter single objective optimization problems testbed. SAHABC algorithm demonstrated competitive performance on the optimization problems with the dimension size of 10, 30, and 50 respectively.

Artificial Bee Colony (ABC) algorithm is one of the most recent swarm intelligence algorithms which used for problem optimization. This paper presents a Hybrid Artificial Bee Colony algorithm (HABC), in which the crossover operator of Genetic Algorithm is introduced, to improve the canonical ABC in solving complex optimization problems. The variation of the algorithm is presented and seven benchmark functions are used to check its validity. The simulation results showed that the proposed HABC outperforms the canonical ABC and Particle Swarm Optimization algorithms on most functions, especially on the multimodal functions.

Artificial Bee Colony (ABC) is a swarm based stochastic search algorithm inspired by the foraging behavior of honeybees. Due to the simplicity of implementation and promising optimization capability, ABC is successfully applied to solve wide class of scientific and engineering optimization problems. But, it has problems of premature convergence and trapping in local optima. In this paper, to enhance the performance of ABC, we have proposed a modified version of ABC algorithm using Differential Evolution (DE) and Polynomial Mutation (PM) called DE-PM-ABC. The comparison with ABC by Karaboga [1], MABC [27] by Liu et al. using some benchmark functions of CEC 2005 demonstrates that our approach achieves a good trade-off between exploration and exploitation and thus obtains better global optimization result and faster convergence speed.

Artificial bee colony (ABC) optimization, ant colony optimization (ACO), and particle swarm optimization (PSO) are well-known swarm intelligence algorithms. They have been widely used for solving many real-life optimization problems in various domains. This chapter presents how these algorithms can be used in optimizing the distributed local area network topology design. The problem has been modelled as a constrained multi-objective optimization problem using goal programming. In addition to adapting the three algorithms for the problem, a hybrid ABC algorithm has also been proposed. Performance of the algorithms has been evaluated through a simulation study, and the results indicate that the hybrid ABC algorithm outperforms ACO, PSO and ABC algorithms.

Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. ABC has gained increasing attention in tackling optimization problems. Its further superiority when hybridized with other techniques is also shown. In this paper a novel hybrid artificial bee colony is proposed in order to solve optimization problems more efficiently, accurately and reliably. During the course of evolvement Extremal Optimization is used to improve the search performance and this makes proposed algorithms have more powerful exploitation capabilities. Simulation and comparisons based on several well-studied benchmarks demonstrate the effectiveness, efficiency and robustness of the proposed algorithms

Swarm intelligence is briefly defined as the collective behavior of decentralized and self-organized swarms. Self-organization and labor division are the two key components of swarm intelligence. Artificial Bee Colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms. The behavior of bees in ABC algorithm satisfies the self-organization features, but there is no specific labor division mechanism in ABC algorithm. In this work, we propose an improved ABC algorithm called labor division artificial bee colony (LDABC) algorithm by incorporating the labor division mechanism into ABC algorithm, which is achieved by individual specialization and role plasticity. We specify three different search methods for employed bees, onlooker bees and scout bees to realize individual specialization, these search methods are related to food source quality, enable bees to maximize exploitation of food source. Role plasticity is achieved by combining with cellular automata, where the roles of bees are not static but vary with their surrounding environment, enable bees not to limit to one search method. The different search modes and the flexibility of the search behaviors make our algorithm achieve a better balance between exploration and exploitation. The experimental results tested on 13 benchmark functions and CEC-2013 test functions demonstrate a competitive performance.

The artificial bee colony (ABC) algorithm, which was inspired by the foraging and dance behaviors of real honey bee colonies, was first introduced for solving numerical optimization problems. When the solution space of the optimization problem is binary-structured, the basic ABC algorithm should be modified for solving this class of problems. In this study, we propose XOR-based modification for the solution-updating equation of the ABC algorithm in order to solve binary optimization problems. The proposed method, named binary ABC (binABC), is examined on an uncapacitated facility location problem, which is a pure binary optimization problem, and the results obtained by the binABC are compared with results obtained by binary particle swarm optimization (BPSO), the discrete ABC (DisABC) algorithm, and improved BPSO (IBPSO). The experimental results show that binABC is an alternative tool for solving binary optimization problems and is a competitive algorithm when compared with BPSO, DisABC, and IBPSO in terms of solution quality, robustness, and simplicity.

Artificial Bee Colony (ABC) algorithm inspired by the complex search and foraging behaviors of real honey bees is one of the most promising implementations of the Swarm Intelligence- (SI-) based optimization algorithms. Due to its robust and phase-divided structure, the ABC algorithm has been successfully applied to different types of optimization problems. However, some assumptions that are made with the purpose of reducing implementation difficulties about the sophisticated behaviours of employed, onlooker, and scout bees still require changes with the more literal procedures. In this study, the ABC algorithm and its well-known variants are powered by adding a new control mechanism in which the decision-making process of the employed bees managing transitions to the dance area is modeled. Experimental studies with different types of problems and analysis about the parallelization showed that the newly proposed approach significantly improved the qualities of the final solutions and convergence characteristics compared to the standard implementations of the ABC algorithms.

The Economic Load Dispatch (ELD) is considered to minimize the only fuel cost in power system planning and operation. Due to environmental issues, the emission amount released by thermal power generation should be considered in minimizing the total cost generation as well. This make the power dispatch problem become multi-objective problem that called Economic and Emission Load Dispatch (EELD). This paper investigates the performances of Artificial Bee colony (ABC) algorithm for determining optimal solution for EELD problem. In this paper, the EELD problem is formulated as a single objective problem by taking minimization of total cost and emission level as separated objective. The cost characteristic of thermal generator considered the effect of opening and closing of the valve for practical application. The ABC algorithm has been tested on standard test systems which is 10-generating unit test system in order to validate the effectiveness of proposed ABC algorithm. The results obtained have been compared with reported result by other methods in literature. From the result, it found that ABC algorithm is better than other methods for minimizing fuel cost of thermal power generation and obtained comparative results in reducing the emission level.

The artificial bee colony (ABC) algorithm is an important branch of evolutionary algorithms and has been shown to be competitive with other algorithms for solving optimisation problems. However, there are always some insufficiencies in ABC algorithm such as lower convergence speed and easily get trapped in the local optima. To increase depth search capabilities of onlooker bees and ensure scout bees do not discard the current optimal solution, we proposed a modified ABC algorithm (denoted as ORABC) based on the optimisation strategy and retained strategy of the best individual in this paper. Compared with ABC algorithm, ABC* (added retained strategy to ABC) and ORABC algorithm, experiments are conducted on a set of numerical benchmark functions. The numerical simulation results demonstrate that ORABC algorithm improves the convergence characteristics of ABC algorithm and provides very remarkable performance in solving complex numerical optimisation problems compared to original algorithm.

Based on the coupled vertical-torsional free vibration records of the spring-suspended section model of bridge decks, an improved Artificial Bee Colony (ABC) algorithm is proposed to identify the flutter derivatives. The ABC algorithm is a novel biological-inspired optimization algorithm, comparing with other iteration methods, the ABC algorithm can facilitate the identification process with no need for initial value. The standard ABC algorithm is good at exploration but poor at exploitation. To enhance the exploitation of the ABC algorithm, Powell’s method is used as a local search tool and a nonlinear factor is introduced for convergence control. Search equations applied to generate candidate solutions in the onlookers and scout bees phase are modified to improve the search ability of this method. The objective function in the optimization is weighted residual sum of squares between measured records and predicted values, where the weighting factors are optimized by an iteration process. The modified ABC algorithm is tested on several benchmark functions compared with the standard ABC algorithm. Simulation results show that the proposed algorithm has a higher convergence speed, better solution quality and stronger robustness. In order to investigate the effectiveness of the modified ABC algorithm in the flutter derivatives identification, numerical simulations of an ideal thin-plate model are carried out. The identification results show that the modified ABC algorithm for flutter derivatives identification of bridge decks is robust and reliable.

The continuous artificial bee colony algorithm for binary optimization

INTRODUCTION: The artificial bee colony (ABC) algorithm is a nature-inspired technique used for solving different optimization problems. This paper presents a multi-hive ABC algorithm for solving constrained benchmark functions of CEC2006. The CEC2006 data set contains the global benchmark functions with different design variables, number and type of constraints. OBJECTIVES: The objective of the proposed work is to design and apply the GPGPU based multi-hive ABC algorithm to solve constrained optimization problems. METHODS: The proposed approach is a multi-population coarse-grained system implemented using General Purpose Graphics Processing Unit (GPGPU). The performance of the proposed approach is compared with the serial ABC algorithm for eleven benchmark functions and results in the literature. The multi-hive ABC algorithm has multiple hives, each running separate ABC algorithm on different cores of GPGPU. RESULTS: The proposed approach provides global best solutions in significantly reduced time for all benchmark functions. The speed-up obtained is approximately 7X to 9X. The GPGPU device utilization is approximately 57% to 91%. CONCLUSION: The GPGPU based multi-hive ABC algorithm is a found good with respect to best results, speed up factor and GPU utilization to solve constrained optimization problems.