Bee Colony Optimization - part I: The algorithm overview

—In this paper an overview of the areas where the Bee Colony Optimization (BCO) and its variants are applied have been given. Bee System was identified by Sato and Hagiwara in 1997 and the Bee Colony Optimization (BCO) was identified by Lucic and Teodorovic in 2001. BCO has emerged as a specialized class of Swarm Intelligence with bees as agents. It is an emerging field for researchers in the field of optimization problems because it provides immense problem solving scope for combinatorial and NP-hard problems. BCO is one of the benchmark systems portraying team work, collaborative work. BCO is a bottom-up approach of modeling where agents form global solution by optimizing the local solution.

Artificial Bee Colony Algorithm (ABC) is nature-inspired metaheuristic, which imitates the foraging behavior of bees. ABC as a stochastic technique is easy to implement, has fewer control parameters, and could easily be modify and hybridized with other metaheuristic algorithms. Due to its successful implementation, several researchers in the optimization and artificial intelligence domains have adopted it to be the main focus of their research work. Since 2005, several related works have appeared to enhance the performance of the standard ABC in the literature, to meet up with challenges of recent research problems being encountered. Interestingly, ABC has been tailored successfully, to solve a wide variety of discrete and continuous optimization problems. Some other works have modified and hybridized ABC to other algorithms, to further enhance the structure of its framework. In this review paper, we provide a thorough and extensive overview of most research work focusing on the app...

Proceedings of the 16th International Conference on Software Technologies, 2021

The foraging behavior of bees has been adapted in a Bee Colony Optimization algorithm (BCO). This approach is a simple and an efficient metaheuristic that has been successfully used to solve many complex optimization problems in different domains, mostly in transportation, location and scheduling fields. In this study, we develop two algorithms for the Daily Car Pooling Problem based on the BCO approach. The developed algorithms are experimentally tested on benchmark instances of different sizes. The computational results show that the proposed approaches can produce good solutions when compared with an exact method.

2015

The Bee Colony Optimization (BCO) algorithm is a nature-inspired meta-heuristic method for dealing with hard, real-life optimization problems. It is based on the foraging habits of honeybees and was proposed by Lučić and Teodorović in 2001. Through numerous applications in transportation, location theory, scheduling and some other fields, method has evolved and underwent many transformations, modifications, even parallelization, which resulted in the increase of its efficiency. The first results of the theoretical verification of the BCO method have appeared recently. The aim of this paper is to further contribute to this topic.

Advanced OR and AI Methods in …, 2005

Handbook of Research on Artificial Intelligence Techniques and Algorithms, 2015

The Bees Algorithm (BA) is a swarm-based optimization algorithm inspired by the food foraging behavior of honeybees. The aim of this chapter is to describe a swarm-based optimization algorithm called the Bees Algorithm and its applications to real world problems. After an explanation of the natural foraging behavior of honeybees, the basic Bees Algorithm and its enhanced version based on Adaptive Neighborhood Search and Site Abandonment (ANSSA) strategy are described and two applications are discussed in detail. The first application deals with the optimization of several benchmark functions and the results obtained by the ANSSA-based BA is compared with the basic BA and other optimization algorithms. The second application deals with the multi-objective optimization problem in finding the best supply chain configuration.

Expert Systems with Applications, 2013

The transit network design problem is one of the most significant problems faced by transit operators and city authorities in the world. This transportation planning problem belongs to the class of difficult combinatorial optimization problem, whose optimal solution is difficult to discover. The paper develops a Swarm Intelligence (SI) based model for the transit network design problem. When designing the transit network, we try to maximize the number of satisfied passengers, to minimize the total number of transfers, and to minimize the total travel time of all served passengers. Our approach to the transit network design problem is based on the Bee Colony Optimization (BCO) metaheuristics. The BCO algorithm is a stochastic, random-search technique that belongs to the class of population-based algorithms. This technique uses a similarity among the way in which bees in nature look for food, and the way in which optimization algorithms search for an optimum of a combinatorial optimization problem. The numerical experiments are performed on known benchmark problems. We clearly show that our approach, based on the BCO algorithm, is competitive with other approaches in the literature, and it can generate highquality solutions.

Swarm Agents are known for their cooperative and collective behavior and operate in decentralized manner which is regarded as Swarm Intelligence. Various techniques like Ant Optimization, Wasp, Bacterial Foraging, PSO, etc., are proposed and implemented in various real-time applications to provide solutions to various real-time problems especially in optimization. The aim of this paper to present ABC algorithm in a comprehensive manner. The ABC-based SI technique proposed has demonstrated that it has superior edge in solving all types of unconstrained optimization problems. Many researchers have fine-tuned the basic algorithm and proposed different ABC based algorithms. The result show that still lots of work is required mathematically and live implementation in order to enable ABC algorithm to be applied to constrained problems for effective solutions.

Artificial Bee Colony Algorithm (ABC) is nature-inspired metaheuristic, which imitates the foraging behavior of bees. ABC as a stochastic technique is easy to implement, has fewer control parameters, and could easily be modify and hybridized with other metaheuristic algorithms. Due to its successful implementation, several researchers in the optimization and artificial intelligence domains have adopted it to be the main focus of their research work. Since 2005, several related works have appeared to enhance the performance of the standard ABC in the literature, to meet up with challenges of recent research problems being encountered. Interestingly, ABC has been tailored successfully, to solve a wide variety of discrete and continuous optimization problems. Some other works have modified and hybridized ABC to other algorithms, to further enhance the structure of its framework. In this review paper, we provide a thorough and extensive overview of most research work focusing on the application of ABC, with the expectation that it would serve as a reference material to both old and new, incoming researchers to the field, to support their understanding of current trends and assist their future research prospects and directions. The advantages, applications and drawbacks of the newly developed ABC hybrids are highlighted, critically analyzed and discussed accordingly.

International Journal of Applied Metaheuristic Computing, 2013

Nowadays computers are used to solve a variety and multitude of complex problems facing in every sphere of peoples’ life. However, many of the problems are intractable in nature exact algorithm might need centuries to manage with formidable challenges. In such cases heuristic or in a broader sense meta-heuristic algorithms that find an approximate solution but have acceptable time and space complexity play indispensable role. In this article, the authors present a state-of-the-art review on meta-heuristic algorithm popularly known as artificial bee colony (ABC) inspired by honey bees. Moreover, the ABC algorithm for solving single and multi-objective optimization problems have been studied. A few potential application areas of ABC are highlighted as an end note of this article.

Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.

A new population-based search algorithm called the Bees Algorithm (BA) is presented. The algorithm mimics the food foraging behaviour of swarms of honey bees. In its basic version, the algorithm performs a kind of neighbourhood search combined with random search and can be used for both combinatorial optimisation and functional optimisation. This paper focuses on the latter. Following a description of the algorithm, the paper gives the results obtained for a number of benchmark problems demonstrating the efficiency and robustness of the new algorithm.

Artificial Intelligence Review, 2016

The Bees Algorithm (BA) is a bee swarm intelligence-based metaheuristic algorithm that is inspired by the natural behavior of honeybees when foraging for food. BA can be divided into four parts: parameter tuning, initialization, local search, and global search. Since its invention, several studies have sought to enhance the performance of BA by improving some of its parts. Thus, more than one version of the algorithm has been proposed. However, upon searching for the basic version of BA in the literature, unclear and contradictory information can be found. By reviewing the literature and conducting some experiments on a set of standard benchmark functions, three main implementations of the algorithm that researchers should be aware of while working on improving the BA are uncovered. These implementations are Basic BA, Shrinking-based BA and Standard BA. Shrinking-based BA employs a shrinking procedure, and Standard BA uses a site abandonment approach in addition to the shrinking procedure. Thus, various implementations of the shrinking and site-abandonment procedures are explored and incorporated into BA to constitute different BA implementations. This paper proposes a framework of the main implementations of BA, including Basic BA and Standard BA, to give a clear picture of these implementations and the relationships among them. Additionally, the experiments show no significant differences among most of the shrinking implementations. Furthermore, this paper reviews the improvements to BA, which are improvements in the parameter tuning, population initialization, local search and global search. It is hoped that this paper will provide researchers who are working on improving the BA with valuable references and guidance.

2016

This paper presents an artificial bee colony algorithm to solve the vehicle routing problem with backhauls and time windows (VRPBTW). This problem is a combination of the vehicle routing problem with backhauls (VRPB) and the vehicle routing problem with time windows (VRPTW). In VRPBTW, a homogenous fleet of vehicles are utilized to deliver goods to customers in linehaul set and then to pick up goods from customers in backhaul set. Vehicle capacity, backhaul and time windows are the major constraints for this problem. The objective of VRPBTW is to minimize the sum of route distance that satisfy all constraints. An artificial bee colony (ABC) algorithm with local search procedures are proposed to solve the modified Solomon's VRPTW benchmark problems. The results of computational experiments reveal that the performance of the proposed ABC algorithm is comparable to the other metaheuristics in terms of the quality of solution.

International Journal on Artificial Intelligence Tools, 2012

Foraging behavior of bees in finding food resource is one of the useful patterns to develop algorithms for solving complex problems. This article by simulation of such behavior and consider a memory for them proposed a method in discrete spaces. The proposed method is applied to Travel Salesman Problem (TSP) and successfully solved it. Simulation results have been proved the performance of our algorithm compared to similar strategies.