A hybrid approach to artificial bee colony algorithm

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).

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.

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 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

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.

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.

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.

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.

Concrete compressive strength (CS) is a vital parameter in structural engineering, yet traditional evaluation methods are time-consuming, destructive, and resource intensive. This study presents a data-driven, computational approach for predicting the CS of fly ash concrete (FAC) using a hybrid Artificial Bee Colony (ABC)-optimized Random Forest (RF) model. The experimental dataset consists of concrete mix designs with varying proportions of fly ash, steel fibers, and water-to-binder ratios, tested over multiple curing ages. Among the regression models, RF algorithm demonstrating the highest predictive accuracy was further improved by tuning its hyperparameters through ABC optimization. The proposed ABC-RF model achieved an R2 of 0.95 and outperformed several state-of-the-art deep learning models. The model also identified that a 30 % fly ash replacement yields optimal compressive strength, a finding further supported by SEM analysis, which showed dense C-S-H matrix formation at this level. The study demonstrates the effectiveness of bio-inspired optimization in civil material modelling and contributes toward sustainable construction practices. The method involves:•Experimental dataset generated from 168 fly ash concrete mix designs with varied input proportions.•Five regression models trained and evaluated using RMSE, MAE, and R2.•Hyperparameters of the RF model optimized using the Artificial Bee Colony algorithm.

Artificial bee colony (ABC) algorithm invented by Karaboga has been proved to be an efficient technique compared with other biological-inspired algorithms for solving numerical optimization problems. Unfortunately, convergence speed of ABC is slow when working with certain optimization problems and some complex multimodal problems. Aiming at the shortcomings, a hybrid artificial bee colony algorithm is proposed in this paper. In the hybrid ABC, an improved search operator learned from Differential Evolution (DE) is applied to enhance search process, and a not-so-good solutions selection strategy inspired by free search algorithm (FS) is introduced to avoid local optimum. Especially, a reverse selection strategy is also employed to do improvement in onlooker bee phase. In addition, chaotic systems based on the tent map are executed in population initialization and scout bee's phase. The proposed algorithm is conducted on a set of 40 optimization test functions with different mathematical characteristics. The numerical results of the data analysis, statistical analysis, robustness analysis and the comparisons with other state-of-the-art-algorithms demonstrate that the proposed hybrid ABC algorithm provides excellent convergence and global search ability.

Harmonic distortion presents a significant challenge in renewable energy integration, particularly in conventional 2-level inverters. Multilevel inverters, such as the cascaded H-bridge topology, offer an effective solution by generating multiple voltage levels, thereby reducing distortion and eliminating the need for bulky filters. This study investigates harmonic reduction in a single-phase 21-level asymmetric cascaded H-bridge multilevel inverter using a novel hybrid optimization algorithm combining Artificial Bee Colony (ABC) and Firefly Algorithm (FA). The hybrid ABC-FA algorithm is designed to determine optimal switching angles for minimizing Total Harmonic Distortion (THD) while addressing the limitations of conventional heuristic methods. Modeled and simulated in MATLAB/SIMULINK, the proposed algorithm demonstrates enhanced performance in both harmonic reduction and convergence speed. Simulation results show that the hybrid ABC-FA algorithm achieves THD levels below 5%, representing a 10–20% improvement over standalone ABC or FA implementations. Additionally, the algorithm exhibits faster convergence, highlighting its effectiveness and reliability for improving power quality and facilitating efficient integration of renewable energy sources into the electrical grid.

This paper presents the comparison results on the performance of the Artificial Bee Colony (ABC) algorithm for constrained optimization problems. The ABC algorithm has been firstly proposed for unconstrained optimization problems and showed that it has superior performance on these kind of problems. In this paper, the ABC algorithm has been extended for solving constrained optimization problems and applied to a set of constrained problems .

Minimizing average lead time for the coordinated scheduling problem in a two-stage supply chain with multiple customers and multiple manufacturers

This paper addresses the usefulness of Artificial Bee Colony (ABC) optimization algorithm for assessment of distribution system reliability. The penalty cost functions are formulated which is related to the failure rate and repair time of each distribution segment. And also, satisfy the reliability constraint such as SAIFI, SAIDI, CAIDI and AENS. The finest values of failure rate and repair time cost function are evaluated using ABC algorithm for reliability enhancement. The performance comparison between ABC and Particle Swarm Optimization (PSO) algorithm also has been done in this paper. In addition, for showing the effectiveness of proposed methodology the numerical results have been compared to the different intelligent techniques that are available in the published literature.

A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems