Artificial bee colony algorithm for large-scale problems and engineering design optimization

Abstract

Engineering design problems are generally large scale or nonlinear or constrained optimization problems. The Artificial Bee Colony (ABC) algorithm is a successful tool for optimizing unconstrained problems. In this work, the ABC algorithm is used to solve large scale optimization problems, and it is applied to engineering design problems by extending the basic ABC algorithm simply by adding a constraint handling technique into the selection step of the ABC algorithm in order to prefer the feasible regions of entire search space. Nine well-known large scale unconstrained test problems and five well-known constrained engineering problems are solved by using the ABC algorithm and the performance of ABC algorithm is compared against those of state-of-the-art algorithms.

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

“…Li and Y i n [94] modified ABC for constrained optimization problems in such a way that employed bees generate solutions based on feasible rule method, whereas onlooker bees perform search in accordance with multi-objective optimization method. A k a y and K a r a b o g a [95] modified the ABC algorithm by incorporating Deb's three heuristic rules to make the feasible search. The resulting improved exploration and exploitation capability of ABC algorithm prove better performance as compared to DE and PSO algorithms on large scale unconstrained optimization problems.…”

A New Meta-Heuristic Technique for Engineering Design Optimization: Seven-Spot Ladybird Algorithm

“…In the experiments, results of the SLO are compared with the Society and Civilization Algorithm (SCA), the Evolution Strategy (μ+λ−ES) and the ABC algorithms [12]. The values of the algorithm-specific control parameters are given in [12].…”

“…The values of the algorithm-specific control parameters are given in [12]. For SLO, the parameters are set the same as those described in section A.…”

“…The best values were recorded to show the ability of an algorithm to find the optimal solution. Table Ⅴ presents the reference optimal values given in [12] and the best values obtained using SLO. Parameter values of the best solutions to the three optimization problems are given in Table Ⅵ, Ⅶ and Ⅷ.…”

“…In recent years, several evolutionary algorithms which are capable of finding the high-precision near-optimal solutions to the large-scale optimization problems have been proposed. Some of the optimization algorithms that are in wide use these days are Genetic Algorithm (GA) [3], Simulated Annealing [4], Tabu Search [5], Covariance Matrix Adaptation Evolution Strategies [6], Teaching-LearningBased Optimization [7], Immune Algorithm [8], Differential Evolution [9], Particle Swarm Optimization (PSO) [10], AntColony Optimization [11], Artificial Bee Colony (ABC) algorithm [12] and some hybrid algorithms [13]. According to the famous No-Free-Lunch theorem for optimization [14], all non-repeating search algorithms have the same mean performance when averaged uniformly over all possible objective functions.…”