Behavior Of Honey Bee Swarm Research Articles

Optimising Routes Scheduling of Garbage Truck for Waste Collection using Artificial Bee Colony Algorithm

The issue of waste generation has attracted significant public attention in today’s societies, not only because waste production is increasing quantitatively but also because some products and components are becoming more complex. The growing problem of municipal solid waste management has become the most serious environmental issue. Population growth has resulted in the increase in the generation of solid waste, which has become a serious issue that must be resolved. Optimised route planning is one of the most important components of a smart waste management system. Route planning is a technological approach to determining the shortest routes through optimisation. The route optimisation problem commonly referred to as the vehicle routing problem (VRP), has been thoroughly studied to find the best possible route for a vehicle. This study aims to find the optimal routes and reduce the distance travelled by the garbage truck when collecting waste using Artificial Bee Colony algorithm. ABC algorithm is an optimization method based on the clever behaviour of honey bee swarms. The artificial bee colony in the ABC algorithm is divided into three groups which are employed bees, onlookers and scouts. To provide neighbouring solutions that are guaranteed to be possible, swap operator is used. Results show that by using 10 trucks in total as the vehicle, the optimal solution produced a total of optimal distance for 94.9542 km. The findings of this study fulfilled the intended objectives which are to find the optimal route and shortest distance for the vehicles.

Regression test optimization and prioritization using Honey Bee optimization algorithm with fuzzy rule base

Regression testing is a maintenance level activity performed on a modified program to instill confidence in the software’s reliability. Prioritization of test case arranges the regression test suite to detect the faults earlier in the testing process. The test cases necessary for validating the recent changes and finding the maximum faults in minimum time are selected. In this manuscript, an optimization algorithm (Bee Algorithm) based on the intelligent foraging behavior of honey bee swarm has been proposed that can enhance the rate of fault detection in test case prioritization. The bee algorithm, along with the fuzzy rule base, reduces the test cases’ volume by selecting the test cases from the pre-existing test suite. The proposed algorithm developed for enhancing the fault detection rate in minimum time is inspired by the behavior of two types of worker bees, namely scout bees and forager bees. These worker bees are responsible for the maintenance, progress, and growth of the colony. The proposed approach is implemented on two projects. The prioritization result is quantified by using the average percentage of fault detection (APFD) metric. Compared with other existing prioritization techniques like no prioritization, reverse prioritization, random prioritization, and previous work, the proposed algorithm outperforms all in fault detection rate. The effectiveness of the proposed algorithm is represented by using the APFD graphs and charts.

Improving the performance of feature selection and data clustering with novel global search and elite-guided artificial bee colony algorithm

As known, the artificial bee colony (ABC) algorithm is an optimization algorithm based on the intelligent foraging behavior of honey bee swarm that has been proven its efficacy and successfully applied to a large number of practical problems. Aiming at the trade-off between convergence speed and precocity of ABC algorithm with elite-guided search equations (ABC_elite), an enhanced version, namely EABC_elite, is proposed in this paper, and the improvements are twofold. As the global best (gbest) solution is introduced to the search equation and acceleration of the convergence in the bee phase of EABC_elite, the former in the ordinary solution is embodied to the search equation yet balance the gbest’s ability. The enhancement to the global search by making the information of gbest and ordinary solutions be adequately used while keeping the exploration–exploitation balance well maintained, the usual solution is introduced to the search equation to avoid the precocity problem in the onlooker bee phase of EABC_elite as the latter one. Experimental analysis and evaluations of EABC_elite against several state-of-the-art variants of the ABC algorithm demonstrate that the EABC_elite is significantly better than the compared algorithms in the feature selection problem. Also, the proposed EABC_elite algorithm is modified to combine the K-means initialization strategy and chaotic parameters strategy to further enhance the global search of EABC_elite for data clustering. Experimental results show that the derived EABC_elite clustering algorithm “Two-step EABC_elite,” TEABC_elite for short, delivered better and promising results than previous works for data clustering.

Chaotic artificial bee colony with elite opposition-based learning

Artificial bee colony (ABC) algorithm is a promising evolutionary algorithm inspired by the foraging behaviour of honey bee swarm, which has obtained satisfactory solutions in diverse applications. However, the basic ABC often demonstrates insufficient exploitation capability in some cases. To address this concerning issue, a chaotic artificial bee colony with elite opposition-based learning strategy (CEOABC) is proposed in this paper. During the search process, CEOABC employs the chaotic local search to promote the exploitation ability. Moreover, the elite opposition-based learning strategy is utilised to exploit the potential information of the exhausted solution. Experimental results compared with several ABC variants show that CEOABC is a competitive approach for global optimisation.

Chaotic artificial bee colony with elite opposition-based learning

Artificial bee colony (ABC) algorithm is a promising evolutionary algorithm inspired by the foraging behaviour of honey bee swarm, which has obtained satisfactory solutions in diverse applications. However, the basic ABC often demonstrates insufficient exploitation capability in some cases. To address this concerning issue, a chaotic artificial bee colony with elite opposition-based learning strategy (CEOABC) is proposed in this paper. During the search process, CEOABC employs the chaotic local search to promote the exploitation ability. Moreover, the elite opposition-based learning strategy is utilised to exploit the potential information of the exhausted solution. Experimental results compared with several ABC variants show that CEOABC is a competitive approach for global optimisation.

NeSS: A modified artificial bee colony approach based on nest site selection behavior

This paper proposes a new version of the Artificial Bee Colony (ABC) optimization algorithm based on the natural nest-site selection behavior of honey bee swarms. The algorithm offers three advantages. First, the responsibility of bees in the proposed algorithm is dynamic, meaning that bee agents may change states (explorer, resting, observer, and committed) during the execution. New feasible solutions are randomly generated in every iteration by varying the numbers of bee agents, thus introducing diversified candidate solutions into the solution space. Second, each bee records its own satisfaction with each nest-site and this satisfaction value is added to the quality measure for the nest site, allowing lower quality nest sites to be retained as candidate solutions and again providing greater diversity. Third, a quorum mechanism provides the stopping criterion, instead of the maximum cycle number (MCN). This mechanism helps the algorithm to converge more quickly. Experiments on the traveling salesman and clustering problems have been conducted in order to observe the algorithm performance. The results show promising performance in solving combinatorial problems when compared with other state-of-the-art algorithms. In addition, the proposed algorithm can converge more quickly with the quorum mechanism.

Aircraft parameter estimation using Hybrid Neuro Fuzzy and Artificial Bee Colony optimization (HNFABC) algorithm

In this paper, method of aerodynamic parameter estimation of aircraft, in the presence of process and measurement noise, have been introduced with the help of relevant experiments carried out on simulated as well as real aircraft data. A merger between two recently proposed artificial intelligence based techniques one of which simulates the intelligent foraging behavior of honey bee swarm viz. Artificial Bee Colony (ABC) optimization along with well-known Adaptive Neuro-Fuzzy System (ANFIS) that simulates the working of a unit of brain viz. neuron combined with the knowledge-based decision making capability of fuzzy system have been shown to be a promisingly new approach to the problem of aerodynamic modeling and parameter estimation for both aerodynamically stable aircraft in the presence of measurement error (sensor noise). Obtained results have been compared with benchmark estimation methods viz. Least Square, Filter Error Method. Corroborating results and comparisons have been furnished highlighting the efficacy of proposed algorithm. Furthermore, it has been shown that the proposed hybrid estimation algorithm can have realizable application in extracting stability and control variables utilizing kinematics of stable aircraft even in absence of adequate information content in the data history.

An improved approximation for the Nakagami-m inverse CDF using artificial bee colony optimization

The quantile function [or inverse cumulative distribution function (CDF)] is a probabilistic measure that is widely employed in both statistical applications and Monte Carlo methods. In addition, this function is important to determine performances of the communication systems, especially for wireless communication systems. However, numerical computing of the Nakagami-m inverse CDF is quite difficult because of the fact that a closed-form expression of the Nakagami-m inverse CDF is not available. In this paper, an improved expression for the Nakagami-m inverse CDF is presented by using curve-fitting methods. Furthermore, parameters of the proposed mathematical model are optimized by the help of artificial bee colony algorithm that is a population based meta-heuristic optimization method motivated by the foraging behavior of honey bee swarms. The results acquired by the proposed approximation are also compared with other existing approaches in the literature in terms of complexity and performance. It is shown that the presented approximation is more accurate, simple and effective against to the previously reported approximations.

Image retrieval via balance-evolution artificial bee colony algorithm and lateral inhibition

Image retrieval is a fundamental issue in pattern recognition. In this work, lateral inhibition (LI) model is adopted as a pre-processing step, which widens the gray level gradients so as to facilitate the image retrieval scheme. In searching for a perfect match between a predefined template and a reference image, we adopt metaheuristic algorithms for good seach capability. Artificial bee colony (ABC) algorithm is a bio-inspired optimization technique, which imitates the foraging behavior of honey bee swarms. It is well known that the algorithm is good at exploration but poor at exploitation. We present balance-evolution artificial bee colony (BE-ABC) algorithm that aims to strike a balance between exploration and exploitation rather than just focusing on improving the latter. BE-ABC algorithm adaptively manipulates the search intensity at the exploration and exploitation stages during the iterations. Besides that, it incorporates an overall degradation procedure to prevent premature convergence. Simulation results confirm that BE-ABC algorithm is more capable than several state-of-the-art metaheuristic algorithms in this image retrieval scheme.

A discrete artificial bee colony algorithm for single machine scheduling problems

This paper presents a discrete artificial bee colony algorithm for a single machine earliness–tardiness scheduling problem. The objective of single machine earliness–tardiness scheduling problems is to find a job sequence that minimises the total sum of earliness–tardiness penalties. Artificial bee colony (ABC) algorithm is a swarm-based meta-heuristic, which mimics the foraging behaviour of honey bee swarms. In this study, several modifications to the original ABC algorithm are proposed for adapting the algorithm to efficiently solve combinatorial optimisation problems like single machine scheduling. In proposed study, instead of using a single search operator to generate neighbour solutions, random selection from an operator pool is employed. Moreover, novel crossover operators are presented and employed with several parent sets with different characteristics to enhance both exploration and exploitation behaviour of the proposed algorithm. The performance of the presented meta-heuristic is evaluated on several benchmark problems in detail and compared with the state-of-the-art algorithms. Computational results indicate that the algorithm can produce better solutions in terms of solution quality, robustness and computational time when compared to other algorithms.

Hybrid Voltage Control Model for Hybrid Renewable Energy System Using Artificial Bees Colony Algorithm

This paper proposed a hybrid control model-based voltage control of Hybrid Energy System (HES) that includes both solar and wind energy conversion systems. The proposed hybrid voltage control model is the combination process of both the Artificial Bees Colony algorithm (ABC) and Proportional Integral controller (PI). The novelty of the proposed method is intelligent foraging behavior of honey bee swarm, which provides random reduction and ability to adapt complex optimization problems. Here, the ABC optimizes the gain parameters under various operating conditions using the minimized Square Error (SE). It considers the actual voltage outcome of the buck converter and the set point voltage. By using the optimized gain parameters, the PI controller is operated. Each separate energy conversion system is controlled by the separate proposed hybrid voltage control model. The proposed method is implemented in MATLAB/Simulink platform and the performance is verified using the comparative analysis with the existing techniques. The comparison results prove the superiority of the proposed method.

Optimum design of cold-formed thin-walled sections subjected to axial and bi-axial bending using artificial bee colony algorithm

Sustainable development in construction industry is emerging as a major issue among cities and communities in the current century. As global climate change becomes an increasingly serious concern for the future and construction industry dependence on fossil fuels for energy creates greater adverse influence on human health and natural environment, an interest in high-efficient, low environmental impact buildings has begun to transform the notion of building design, construction, and operation. As it stands, the most of the standard buildings in the world consume an extraordinary amount of resources while taking an enormous toll on the environment. The utilization of cold-formed thin-walled open steel sections in structural sites supplies green structural opportunities demanding less material and cost while providing high strength. The developed algorithm for this study obtains the optimum geometric dimensions of cold-formed thin-walled open steel sections under various external loading. Moreover, this design algorithm takes into account of the effect of geometric nonlinearity as well as effect of warping. Also the displacement and stress constraints are included in the formulation of the design problem. The optimum design problem obtained turn out to be mixed integer and discrete programming problem. Artificial Bee Colony (ABC) algorithm is used to obtain its solution. This technique is a recent numerical optimization technique which mimics the intelligent behavior of honey bee swarm. The recent studies with the ABC method have shown its effectiveness and robustness in finding the optimum solution of combinatorial optimization problems. A design example is included to demonstrate the efficiency of the optimum design algorithm developed.

An adaptive artificial bee colony algorithm for global optimization

Artificial bee colony algorithm (ABC) is a recently introduced swarm based meta-heuristic algorithm. ABC mimics the foraging behavior of honey bee swarms. Original ABC algorithm is known to have a poor exploitation performance. To remedy this problem, this paper proposes an adaptive artificial bee colony algorithm (AABC), which employs six different search rules that have been successfully used in the literature. Therefore, the AABC benefits from the use of different search and information sharing techniques within an overall search process. A probabilistic selection is applied to determine the search rule to be used in generating a candidate solution. The probability of selecting a given search rule is further updated according to its prior performance using the roulette wheel technique. Moreover, a memory length is introduced corresponding to the maximum number of moves to reset selection probabilities. Experiments are conducted using well-known benchmark problems with varying dimensionality to compare AABC with other efficient ABC variants. Computational results reveal that the proposed AABC outperforms other novel ABC variants.

A hybrid artificial bee colony algorithm for the job-shop scheduling problem with no-wait constraint

This paper studies a hybrid artificial bee colony (ABC) algorithm for finding high quality solutions of the job-shop scheduling problem with no-wait constraint (JSPNW) with the objective of minimizing makespan among all the jobs. JSPNW is an extension of well-known job-shop scheduling problem subject to the constraint that no waiting time is allowed between operations for a given job. ABC algorithm is a swarm intelligence technique based on intelligent foraging behavior of honey bee swarm. The proposed hybrid approach effectively coordinates the various components of ABC algorithm such as solution initialization, selection and determination of a neighboring solution with the local search in such a way that it leads to high quality solutions for the JSPNW. The proposed approach is compared with the two best approaches in the literature on a set of benchmark instances. Computational results show the superiority of the proposed approach over these two best approaches.

A novel artificial bee colony algorithm based on modified search strategy and generalized opposition-based learning

Artificial bee colony ABC algorithm, which is inspired by the foraging behavior of honey bee swarm, is a biological-inspired optimization algorithm. It shows more effective than genetic algorithm G...

Enhanced ABC Based PID Controller for Nonlinear Control Systems

A Nonlinear PID (NPID) controller tuning based on the Enhanced Artificial Bee Colony (E-ABC) algorithm is presented. The ABC algorithm uses the foraging behavior of honey bee swarm to find the optimal PID parameters kp, ki and kd. In this proposed E-ABC, the Particle Swarm Optimization (PSO) swarm intelligence behavior is inherited to ABC scout bee to get proper selection of food source. The convergence characteristics of the E-ABC based optimization shows that the proposed method provides better controller settings with minimum iteration. To show the effectiveness of the proposed method, it is presented to the nonlinear Continuous Stirred Tank Reactor (CSTR) process and the results are compared with the conventional Internal Model Control (IMC) tuning method, and heuristic approaches viz., Genetic Algorithm (GA), Simulated Annealing (SA), PSO and other hybrid methods based PID performances. From the results of integral performance criterions viz., ISE, IAE and ITAE, it is evident that the proposed E-ABC provides better tracking and improves closed loop accuracy.

Hybridizing Artificial Bee Colony Algorithm with Multi-Parent Crossover Operator

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.

Artificial Bee Colony Algorithm for Solving OPF Problem Considering the Valve Point Effect

icial Bee Colony Algorithm (ABC) is a viable optimization algorithm, based on simulating of the foraging behavior of honey bee swarm. This paper is examined the ability of Artificial Bee Colony algorithm for solving the Optimal Power Flow (OPF) problem considering the valve point effects in a power systems. The objective functions considered are: fuel cost minimization, the valve point effect and multi-fuel of generation units. The proposed algorithm is applied to determine the optimal settings of OPF problem control variables. The feasibility of the proposed algorithm has been tested on the IEEE 30-bus and IEEE-57 bus test systems, with different objective functions. Several cases were investigated to test and validate the robustness of the proposed algorithm in finding the optimal solution or the near optimal solution for each objective. Moreover, the obtained results are compared with those available recently in the literature. Therefore, the ABC algorithm could be a useful algorithm for implementation in solving the OPF problem. KeywordsPower Flow (OPF), Artificial Bee Colony algorithm

A hybrid swarm intelligence approach to the registration area planning problem

Proper management of locations of the mobile devices is of great importance in this era of wireless communication networks, as the use of the mobile devices has grown exponentially. The registration area planning (RAP) problem aims to group the wireless network cells into contiguous areas called registration areas in order to minimize the cost of managing the location of mobile devices. Since the RAP problem is a grouping problem, therefore, a grouping-based artificial bee colony algorithm coupled with a local search is developed for this problem. An artificial bee colony algorithm is a recently developed swarm intelligence technique based on the intelligent foraging behavior of honey bee swarm. We have compared the proposed approach with the state-of-the-art approaches available in the literature. Computational results clearly show the superiority of the proposed approach in comparison to these approaches in terms of both the solution quality and the running time.

Swarm intelligence approaches for cover scheduling problem in wireless sensor networks

Wireless sensor networks WSNs are getting more and more attention these days. Already, innumerable approaches have been proposed to solve various problems in WSNs. In this paper, we have proposed two swarm intelligence approaches, viz. artificial bee colony ABC algorithm and invasive weed optimisation IWO algorithm for the cover scheduling problem in WSNs where coverage breach is allowed either due to technical constraints or deliberately. The objective of the wireless sensor network cover scheduling problem WSN-CSP is to schedule the covers of sensors in such a manner so that the longest target breach is minimised. The WSN-CSP is an NP-Hard problem and is relatively under-studied. ABC algorithm is based on intelligent foraging behaviour of honey bee swarms, whereas IWO algorithm is based on colonising behaviour of weeds. For further improving the results obtained through ABC and IWO approaches, we have also devised a local search. Computational results show the effectiveness of our proposed approaches in comparison to a genetic algorithm and a problem specific heuristic available in the literature.