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A Population-Based extremal Optimization Algorithm with Knowledge-Based mutation

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Date

2014

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Abstract

Extremal optimization is a dynamic, heuristic intelligent algorithm. It evolves a single solution and makes local modifications to the worst components. In this paper, a knowledge-base mutation operator is presented based on the distribution knowledge of candidate solutions. And then a population-based extremal optimization with knowledge-based mutation is proposed by introducing the idea of swarm evolution. Finally, the proposed method is applied to PID parameter tuning. The simulation results show that the proposed algorithm is characterized by high response speed, small overshoot and steady-state error, and obtains satisfactory control effect.

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Keywords

Extremal optimization, Knowledge-based mutation, PID controller

Citation

URI

https://dspace-test.anu.edu.au/handle/1885/733729110

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DevOps Pure Testing

Source

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Article

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DOI

10.1007/978-3-319-11857-4_11

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