Optimization algorithm based on biology life cycle theory

Authors:
Hai Shen;Ben Niu;Yunlong Zhu;Hanning Chen
Affiliations:
College of Physics and Technology, Shenyang Normal University, Shenyang, China;College of Management, Shenzhen University, Shenzhen, China;Laboratory of Information Service and Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences Shenyang, Shenyang, China;Laboratory of Information Service and Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences Shenyang, Shenyang, China
Venue:
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Year:
2013

Citing 5
Cited 0

Artficial Immune Systems and Their Applications

Artficial Immune Systems and Their Applications
Ant colony optimization theory: a survey

Theoretical Computer Science
Evolutionary programming made faster

IEEE Transactions on Evolutionary Computation
Data mining with an ant colony optimization algorithm

IEEE Transactions on Evolutionary Computation
Bacterial foraging based approaches to portfolio optimization with liquidity risk

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Bio-inspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biology life cycle, this paper presents a novel optimization algorithm called Lifecycle-based Swarm Optimization. LSO algorithm simulates biologic life cycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection and mutation. Experiments were conducted on 7 unimodal functions. The results demonstrate remarkable performance of the LSO algorithm on those functions when compared to several successful optimization techniques.