An Optimization Framework of Multiobjective Artificial Bee Colony Algorithm Based on the MOEA Framework

Hindawi Computational Intelligence and Neuroscience Volume 2018, Article ID 5865168, 26 pages https://doi.org/10.1155/2018/5865168 Research Article An Optimization Framework of Multiobjective Artificial Bee Colony Algorithm Based on the MOEA Framework Jiuyuan Huo 1,2 and Liqun Liu 3 1 School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China 3 College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China 2 Correspondence should be addressed to Jiuyuan Huo; Received 11 June 2018; Revised 10 September 2018; Accepted 27 September 2018; Published 1 November 2018 Academic Editor: Daniele Bibbo Copyright © 2018 Jiuyuan Huo and Liqun Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The artificial bee colony (ABC) algorithm has become one of the popular optimization metaheuristics and has been proven to perform better than many state-of-the-art algorithms for dealing with complex multiobjective optimization problems. However, the multiobjective artificial bee colony (MOABC) algorithm has not been integrated into the common multiobjective optimization frameworks which provide the integrated environments for understanding, reusing, implementation, and comparison of multiobjective algorithms. Therefore, a unified, flexible, configurable, and user-friendly MOABC algorithm framework is presented which combines a multiobjective ABC algorithm named RMOABC and the multiobjective evolution algorithms (MOEA) framework in this paper. The multiobjective optimization framework aims at the development, experimentation, and study of metaheuristics for solving multiobjective optimization problems. The framework was tested on the Walking Fish Group test suite, and a many-objective water resource planning problem was utilized for verification and application. The experiment’s results showed the framework can deal with practical multiobjective optimization problems more effectively and flexibly, can provide comprehensive and reliable parameters sets, and can complete reference, comparison, and analysis tasks among multiple optimization algorithms. 1. Introduction The optimization problems in the real world are multiobjective in nature, which means that the optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. These problems are known as multiobjective optimization problems (MOPs) which can be found in many disciplines such as engineering, transportation, economics, medicine, and bioinformatics [1]. Most of the multiobjective techniques have been designed based on the theories of Pareto Sort [2] and nondominated solutions. Thus, the optimum solution for this kind of problem is not a single solution as in the mono-objective case, but rather a set of solutions known as the Pareto optimal set. This refers to when no element in the set is superior to the others for all the objectives. By using the multiobjective optimization method, the conflicting objectives in these MOPs can acquire better trade-off, and satisfactory optimization results can be given. Therefore, with the complexity and nonlinearity of objectives and constraints, finding a set of good quality nondominated solutions becomes more challenging, and research of efficient and stable multiobjective optimization algorithms is also one of the key and major directions for scholars to study. Over the last few decades, the metaheuristics algorithms [3] have proven to be effective methods for solving MOPs. Among them, the evolutionary algorithms are very popular and widely used to effectively solve complex real-world MOPs [4]. Some of the most well-known algorithms belong to this class, such as the Nondominated Sorted Genetic Algorithm-II (NSGA-II) [5], Multiobjective ε-evolutionary Algorithm based on ε Dominance (ε-MOEA) [6], and Borg [7]. Nevertheless, the swarm intelligence algorithm [8] inspired by biological information is one important type of metaheuristic algorithms. With its unique advantages and mechanisms, it has become a popular and important field. The main algorithms include the particle swarm optimization 2 (PSO) algorithm [9], ant colony optimization (ACO) algorithm [10], and shuffled frog leaping algorithm (SFLA) [11]. In 2005, Karaboga proposed an artificial bee colony (ABC) algorithm based on the foraging behavior of honeybees [12]. ABC has been demonstrated to have a strong ability to solve optimization problems, and its validity and practicality have been proven [13]. Because of achieving high convergence speed and strong robustness, it has been used in different areas of engineering and seems more suitable for multiobjective optimization. At present, the ABC algorithm and its application research mainly focuses on single-objective optimization. The study of multiobjective optimization has just begun. However, because the multiobjective optimization needs to cope with real problems, there exists some inconvenience in practical applications. For instance, the multiobjective optimization algorithms are closely related to solving problems which are difficult to apply to other MOPs; a consistent model is needed to regulate and compare optimization strategies of different multiobjective optimization algorithms, and users have difficulty choosing the suitable optimization algorithm for their problems and also need to spend a lot of time learning the algorithms. In this context, it is necessary to establish a unified, universal, and user-friendly multiobjective optimization framework which can be a valuable tool for understanding the behavior of existing techniques, for codes or modules that reuse existing algorithms, and for helping in the implementation and comparison of algorithms’ new ideas. Moreover, researchers have found that focusing on the study of one algorithm has a lot of limitations. If different heuristic algorithms can be effectively referred or integrated with each other, they can handle actual problems or large-scale problems more effectively and more flexibly [14]. Therefore, multiobjective optimization frameworks have been proposed to integrate optimization algorithms, optimization problems, evaluation functions, improvement strategies, adjustment methods, and output of results to provide an integrated environment for users to easily handle optimization problems, such as the jMetal [15], Paradiseo-MOEO [16], and PISA [17]. Among them, the MOEA framework [18] is a powerful and efficient platform which is a free and open source Java library for developing and experimenting with multiobjective evolutionary algorithms (MOEAs) and other general purpose multiobjective optimization algorithms. However, in these integrated environments f (...truncated)