A fitness approximation assisted competitive swarm optimizer for large scale expensive optimization problems

Surrogate assisted meta-heuristic algorithms have received increasing attention over the past years due to the fact that many real-world optimization problems are computationally expensive. However, most existing surrogate assisted meta-heuristic algorithms are designed for small or medium scale pro...

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Bibliographic Details
Published in:Memetic computing 2018-06, Vol.10 (2), p.123-134
Main Authors: Sun, Chaoli, Ding, Jinliang, Zeng, Jianchao, Jin, Yaochu
Format: Article
Language:English
Subjects:
Algorithms
Applications of Mathematics
Approximation
Artificial Intelligence
Bioinformatics
Competition
Complex Systems
Computation
Control
Engineering
Evolutionary algorithms
Fitness
Heuristic methods
Mathematical and Computational Engineering
Mechatronics
Optimization
Regular Research Paper
Robotics
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Summary:Surrogate assisted meta-heuristic algorithms have received increasing attention over the past years due to the fact that many real-world optimization problems are computationally expensive. However, most existing surrogate assisted meta-heuristic algorithms are designed for small or medium scale problems. In this paper, a fitness approximation assisted competitive swarm optimizer is proposed for optimization of large scale expensive problems. Different from most surrogate assisted evolutionary algorithms that use a computational model for approximating the fitness, we estimate the fitness based on the positional relationship between individuals in the competitive swarm optimizer. Empirical study on seven widely used benchmark problems with 100 and 500 decision variables show that the proposed fitness approximation assisted competitive swarm optimizer is able to achieve competitive performance on a limited computational budget.
ISSN:1865-9284
1865-9292
DOI:10.1007/s12293-016-0199-9