Impact Score 2.15
A Special Issue in Memetic ComputingOpen to submissions until December 31 2021
Over the last few decades, multi-objective optimization has drawn a lot of attention due to its significance in many real-world applications, such as business, management, and engineering. Multi-objective optimization involves optimizing at least two conflicting objective functions simultaneously, probably subject to a set of constraints. Unlike single-objective optimization, there is no single solution that can be optimal for all the objective functions. Instead, the aim in this case is to find a set of trade-off solutions called Pareto optimal. The set of all the Pareto optimal solutions is called the Pareto set and its image in the objective space is called the Pareto front. The aim of the multi-objective optimization algorithms is to find a set of solutions for different optimal trade-offs among the multiple objectives. The objective vectors of these solutions can properly approximate the true Pareto front of a problem.
Solving multi-objective optimization problems in an exact manner is impossible in most cases, but the use of memetic algorithms provides a very flexible and successful possibility of generating high-quality approximate solutions. By combining population-based multi-objective optimization algorithms with improvement techniques such as local search strategies and individual learning procedures, the capability of algorithms for refining solutions can be substantially enhanced. These algorithms exhibit good performance on various benchmark problems and real-world applications.
As with problem-dependent improvement techniques, generating Pareto optimal solutions by memetic algorithms pose several particular issues such as the proper algorithmic design and analysis. Besides, some scholars have pointed out that the performance comparison via a large number of experiment tests cannot reveal the real strengths and weaknesses of multi-objective memetic algorithms. In particular, a few recent studies have shown that the good performance of some algorithms depends on the special characteristics of the test problems adopted.
This Special Issue will accept original research and review articles on novel multi-objective memetic techniques and their applications. We also welcome analysis and design of multi-objective optimization test problems, as well as performance assessment indicators
Topics of interest include, but are not limited to:
Key DatesSubmission deadline: December 31, 2021First-round review result: February 28, 2022Revision deadline: April 30, 2022Second-round review result: Jun 30, 2022Final acceptance: July 31, 2022
Guest EditorsZhenkun Wang, Southern University of Science and Technology, ChinaMan-Fai Leung, The Open University of Hong Kong, ChinaHangjun Che, Southwest University, ChinaCarlos A. Coello Coello, CINVESTAVIPN, Mexico