Teng Nga Sing

Universiti of Malaysia Sabah

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Jason Teo

Universiti of Malaysia Sabah

Mohd Hanafi Ahmad Hijazi

Universiti of Malaysia Sabah

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Universiti of Malaysia Sabah

Yes Technologies (United States)

Qiqihar University

Information Technology University

DSO National Laboratories

American Institute of Aeronautics and Astronautics

Massachusetts Institute of Technology

Agency for Science, Technology and Research

Centre for Artificial Intelligence and Robotics

Institute of Microelectronics

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Evolutionary and population dynamics of 3 parents differential evolution (3PDE) using self-adaptive tuning methodologies

Natural Computing (2010)

Teng Nga Sing Jason Teo

Differential Evolution

Evolutionary Dynamics

Constant (computer programming)

10.1007/s11047-010-9194-0

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Empirical testing on 3-Parents Differential Evolution (3PDE) for unconstrained function optimization

Teng Nga Sing Jason Teo Mohd Hanafi Ahmad Hijazi

The objective of this paper is to investigate whether the performance of the self-adaptive the parameters in 3PDE can improve the performance for function optimization. In this paper, we firstly propose three new algorithms (3PDE SACr, 3PDE-SAF and 3PDE-SACrF). The preliminary testing is carried out to compare their performance with 3PDE to determine the best algorithm for the next step to self-adapt the population size. Here, the best algorithm from the preliminary testing will be chosen for the testing on self-adapting the population size in absolute and relative encodings. The preliminary testing showed that 3PDE-SAF performed the best for the first three proposed algorithms. So, 3PDE-SAF is chosen for the self-adaptive population size to test in absolute (3PDE-SAF-Abs) and relative (3PDE-SAF-Rel) encodings and the final result showed that 3PDE-SAF-Rel performed slightly better than all the proposed algorithms in terms of its average performance and its stability.

Differential Evolution

10.1109/cec.2007.4424752

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Self-adaptive parameterization using 3-parents differential evolution

International MultiConference of Engineers and Computer Scientists (2007)

Teng Nga Sing Jason Teo Mohd Hanafi Ahmad Hijazi

The aim of 3-Parents Differential Evolution (3PDE) is to reduce the parental requirement in the original Differential Evolution (DE). The effectiveness of 3PDE has been reported and is considered as a useful algorithm that performed better convergence to optimality. In general, 3PDE is considered as a useful contribution since it has successfully reduced the parental requirement in DE without significant reductions in absolute optimization performance by gaining better average performance as well as stability. The objective of this paper is to investigate whether certain parameters that are self-adapted in 3PDE can enhance its performance for function optimization. Here, we propose three new algorithms to compare against 3PDE for their performance, which included 3SACr, 3SAF, and 3SAFCr. Fifty run were conducted for each 20 well-known benchmark functions to test all proposed algorithms. The experimental results showed that 3SACr performed the best among the other algorithms in terms of its better average performance as well stability.

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Differential Evolution

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