TY - GEN
T1 - Macroscopic Indeterminacy Swarm Optimization (MISO) algorithm for real-parameter search
AU - Chang, Po Chun
AU - He, Xiangjian
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/16
Y1 - 2014/9/16
N2 - Swarm Intelligence (SI) is a nature-inspired emergent artificial intelligence. They are often inspired by the phenomena in nature. Many proposed algorithms are focused on designing new update mechanisms with formulae and equations to emerge new solutions. Despite the techniques used in an algorithm being the key factor of the whole system, the evaluation of candidate solutions also plays an important role. In this paper, the proposed algorithm Macroscopic Indeterminacy Swarm Optimization (MISO) presents a new search scheme with indeterminate moment of evaluation. Here, we perform an experiment based on public benchmark functions. The results produced by MISO, Differential Evolution (DE) with various settings, Artificial Bee Colony (ABC), Simplified Swarm Optimization (SSO), and Particle Swarm Optimization (PSO) have been compared. The result shows MISO can achieve similar or even better performance than other algorithms.
AB - Swarm Intelligence (SI) is a nature-inspired emergent artificial intelligence. They are often inspired by the phenomena in nature. Many proposed algorithms are focused on designing new update mechanisms with formulae and equations to emerge new solutions. Despite the techniques used in an algorithm being the key factor of the whole system, the evaluation of candidate solutions also plays an important role. In this paper, the proposed algorithm Macroscopic Indeterminacy Swarm Optimization (MISO) presents a new search scheme with indeterminate moment of evaluation. Here, we perform an experiment based on public benchmark functions. The results produced by MISO, Differential Evolution (DE) with various settings, Artificial Bee Colony (ABC), Simplified Swarm Optimization (SSO), and Particle Swarm Optimization (PSO) have been compared. The result shows MISO can achieve similar or even better performance than other algorithms.
KW - artifical intelligence
KW - evolution strategies
KW - evolutionary algorithm
KW - global optimization
KW - swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=84908574240&partnerID=8YFLogxK
U2 - 10.1109/CEC.2014.6900281
DO - 10.1109/CEC.2014.6900281
M3 - Conference contribution
AN - SCOPUS:84908574240
T3 - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
SP - 1571
EP - 1578
BT - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Y2 - 6 July 2014 through 11 July 2014
ER -