Bee Colony Optimization for Single and Multi-Objective Numerical Optimization

One common feature of natural systems is the ability for the dynamic interaction between the most basic individual organisms to produce systems capable of performing complex tasks. This thesis introduces a novel population based search algorithm known as Bees Algorithm (BA), that simulates the manner in which swarms of honey bees forage for food. This algorithm involves a collection of a neighborhood and stochastic search and is used in both functional and combinatorial optimization. After describing the algorithm in detail, this thesis attempts to elucidate the robustness and efficiency of the algorithm based on the outcomes for a library of complex numerical optimization problems. The Artificial Bee Colony (ABC) is a swarm based on meta-heuristic algorithm used to optimize numerical optimization problems and provide accurate solutions. The use of the term ‗meta-heuristic‘ here refers to the capacity of the algorithm to provide optimal solutions even in cases on imperfect or incomplete information. Bee colonies scour many sources of food to determine the best source based on a number of parameters, such as time, the amount and quality of nectar, etc. In a similar manner, models that use the ABC algorithm are composed of three components: Unemployed bees, Employed bees, and Food sources (Fitness). The employed bees are responsible for finding affluent sources of food close to the hive. In the algorithm, artificial forager bees acting as environmental agents search for rich food sources. The process of applying the algorithm begins with transforming the given optimization problem into one of examining the best parameter vectors, from a population of vectors, to minimize the objective function. Starting with population of preliminary solution vectors, potential solutions are enhanced using certain strategies. This thesis work introduces a Bee Colony Optimization Algorithm and examines its feasibility based on the results of CEC'05 and CEC'17 expensive benchmark problems for single objective optimization problems , and used CEC'09 and CEC'18 expensive benchmark problem for Multi-objective optimization. The methods used in our studies are compared to different well-knows methods proposed in the related literature was conducted. The final ranking of all test problems indicate that BCO was always among the top best algorithms that were used for the same purpose. Keywords: Multi-agent systems, Meta-heuristic algorithms, Multi-objective optimization, Swarm intelligence, Pareto optimality