A new hybrid algorithm for optimization based on Artificial Bee Colony and Extremal Optimization

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 metaheuristic approach in which a colony of artificial bees cooperates in finding good solutions for numerical optimization problems. ABC is adopted widely for use in several domains of solution optimization. However, the algorithm generally requires a considerably large computational time and resources. In order to enhance the performance of this algorithm for a large problem size, we introduce a distributed version of ABC. In our parallel algorithm, the entire bee colony is decomposed into several subgroups. Each subgroup then performs a local search concurrently on each processor node. The local best solutions are then exchanged among processor nodes. The algorithm implementation utilizes the message passing technique as a communication paradigm. We then empirically assess the performance based on both result accuracy and algorithm's efficiency. The experimental results show improvement in both solution quality and computing time when comparing to the sequential ABC algorithm.

Multiple sequences alignment is an essential task in many bioinformatics analyses. However, it is a challenging problem due to its exponential complexity. Although many approaches have been proposed for solving the MSA problem, finding the best alignment of the MSA remains an open problem. In this work, we propose a new multiobjective optimization approach based on the Artificial Bee Colony (ABC) algorithm to solve the MSA problem. The ABC algorithm is inspired by the intelligent behavior of honey bee swarms. Our proposed approach optimizes two objective functions; the sum-of-pairs (SP) function and entropy. Optimizing these objective functions represents a trade off between maximizing the SP function and minimizing the entropy to choose the most suitable alignment. Experiments are conducted on 12 datasets from BAliBASE 3.0. The experimental results show that our proposed approach achieves better alignments than Clustal in four of the tested datasets. In general, the proposed approach performs better on RV12 datasets.

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) 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.

This work integrates artificial bee colony (ABC) and bees algorithm (BA) to develop a two bees (TB) algorithm. Agents of TB are stochastically assigned to ABC and BA sub-swarms in each iteration according to their fitness values. Consequently, the current healthier sub-swarm gains more agents to carry out its actions. Sub-swarm populations therefore vary ceaselessly during iterations, while either the ABC or BA sub-swarms may be superior to the other. Experiments are conducted on 23 benchmark functions. Results demonstrate that the TB performs better than or close to the ABC or BA winner. TB overcomes the poor performance of ABC and BA in handling particular problems.

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.

Artificial Bee Colony algorithm (ABC) is a new optimization algorithms used to solve several optimization problems. The algorithm is a swarm-based that simulates the intelligent behavior of honey bee swarm in searching for food sources. Several variations of ABC have been three existing solution vectors, the new solution vectors will replace the worst three vectors in the food source proposed to enhance its performance. This paper proposes a new variation of ABC that uses multi-parent crossover named multi parent crossover operator artificial bee colony (MPCO-ABC). In the proposed technique the crossover operator is used to generate three new parents based on memory (FSM). The proposed algorithm has been tested using a set of benchmark functions. The experimental results of the MPCO-ABC are compared with the original ABC, GABC. The results prove the efficiency of MPCO-ABC over ABC. Another comparison of MPCO-ABC results made with the other variants of ABC that use crossover and/or mutation operator. The MPCO-ABC almost always shows superiority on all test functions.

Cloud computing is increasing rapidly as a successful paradigm presenting on-demand infrastructure, platform, and software services to clients. Load balancing is one of the important issues in cloud computing to distribute the dynamic workload equally among all the nodes to avoid the status that some nodes are overloaded while others are underloaded. Many algorithms have been suggested to perform this task. Recently, worldview is turning into a new paradigm for optimization search by applying the osmosis theory from chemistry science to form osmotic computing. Osmotic computing is aimed to achieve balance in highly distributed environments. The main goal of this paper is to propose a hybrid metaheuristics technique which combines the osmotic behavior with bio-inspired load balancing algorithms. The osmotic behavior enables the automatic deployment of virtual machines (VMs) that are migrated through cloud infrastructures. Since the hybrid artificial bee colony and ant colony optimization proved its efficiency in the dynamic environment in cloud computing, the paper then exploits the advantages of these bio-inspired algorithms to form an osmotic hybrid artificial bee and ant colony (OH_BAC) optimization load balancing algorithm. It overcomes the drawbacks of the existing bio-inspired algorithms in achieving load balancing between physical machines. The simulation results show that OH_BAC decreases energy consumption, the number of VMs migrations and the number of shutdown hosts compared to existing algorithms. In addition, it enhances the quality of services (QoSs) which is measured by service level agreement violation (SLAV) and performance degradation due to migrations (PDMs).

Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. The standard ABC is weak at the local-search capability and precision. Extremal Optimization (EO) is a general-purpose heuristic method which has strong local-search capability and has been successfully applied to a wide variety of hard optimization problems. In order to strengthen the local-search capability of ABC, this work proposes a novel hybrid optimization method, called ABC-EO algorithm, through introducing EO to ABC. The simulation results show that the performance of the proposed method is as good as or superior to those of the state-of-the-art algorithms in complex numerical optimization problems.

With the advent of cloud manufacturing (CMfg), more and more services in CMfg platforms may provide the same functionality but differ in performance. In order to insure the manufacturing cloud to match the complicated task requirements, composited CMfg service optimal selection (CCSOS) is becoming increasingly important. This study proposes a new approach for such CCSOS problems, the so-called hybrid artificial bee colony (HABC) algorithm, which employs both the probabilistic model of Archimedean copula estimation of distribution algorithm (ACEDA) and the chaos operators of global best-guided artificial bee colony to generate the offspring individuals with consideration of quality of service (QoS) and CMfg environment. Different-scale CCSOS problems are adopted to evaluate the performance of the proposed HABC. Experimental results have shown that the HABC can find better solutions compared with such algorithms as genetic algorithm, particle swarm optimization, and basic artificial bee colony algorithm.

In this paper, we put forward a hybrid approach based on the life cycle for the artificial bee colony algorithm to generate dynamical varying population as well as ensure appropriate balance between exploration and exploitation. The bee life-cycle model is firstly constructed, which means that each individual can reproduce or die dynamically throughout the searching process and population size can dynamically vary during execution. With the comprehensive learning, the bees incorporate the information of global best solution into the search equation for exploration, while the Powell's search enables the bees deeply to exploit around the promising area. Finally, we instantiate a hybrid artificial bee colony (HABC) optimizer based on the proposed model, namely HABC. Comprehensive test experiments based on the well-known CEC 2014 benchmarks have been carried out to compare the performance of HABC against other bio-mimetic algorithms. Our numerical results prove the effectiveness of the proposed hybridization scheme and demonstrate the performance superiority of the proposed algorithm.