A survey on evolutionary computation for complex continuous optimization

Zhi‐Hui Zhan

doi:10.1007/s10462-021-10042-y

2021-07-27

194

DOI：10.1007/s10462-021-10042-y

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摘要

En 中文

Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

Keyword:

Evolutionary computation (EC)

Evolutionary algorithm (EA)

Swarm intelligence (SI)

Complex continuous optimization problems

Large-scale optimization

Dynamic optimization

Multi-modal optimization

Many-objective optimization

Constrained optimization

Expensive optimization

Function-oriented taxonomy