Developmental Swarm Intelligence

2016 ◽  
pp. 349-367
Author(s):  
Yuhui Shi
Keyword(s):  
Brain Development ◽  
Evolutionary Algorithm ◽  
Swarm Intelligence ◽  
Brain Evolution ◽  
Developmental Learning ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

In this article, the necessity of having developmental learning embedded in a swarm intelligence algorithm is confirmed by briefly considering brain evolution, brain development, brainstorming process, etc. Several swarm intelligence algorithms are looked at from developmental learning perspective. Finally, a framework of a developmental swarm intelligence algorithm is given to help understand developmental swarm intelligence algorithms, and to guide to design and/or implement any new developmental swarm intelligence algorithm and/or any developmental evolutionary algorithm.

Developmental Swarm Intelligence

2014 ◽  
Vol 5 (1) ◽  
pp. 36-54 ◽  
Author(s):  
Yuhui Shi
Keyword(s):  
Brain Development ◽  
Evolutionary Algorithm ◽  
Swarm Intelligence ◽  
Brain Evolution ◽  
Developmental Learning ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

In this paper, the necessity of having developmental learning embedded in a swarm intelligence algorithm is confirmed by briefly considering brain evolution, brain development, brainstorming process, etc. Several swarm intelligence algorithms are looked at from developmental learning perspective. Finally, a framework of a developmental swarm intelligence algorithm is given to help understand developmental swarm intelligence algorithms, and to guide to design and/or implement any new developmental swarm intelligence algorithm and/or any developmental evolutionary algorithm.

Unified Swarm Intelligence Algorithms

2018 ◽  
pp. 1-26 ◽  
Author(s):  
Yuhui Shi
Keyword(s):  
Brain Development ◽  
Evolutionary Algorithm ◽  
Swarm Intelligence ◽  
Brain Evolution ◽  
Learning Stage ◽  
Developmental Learning ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

In this chapter, the necessity of having developmental learning embedded in a swarm intelligence algorithm is confirmed by briefly considering brain evolution, brain development, brainstorming process, etc. Several swarm intelligence algorithms are looked at from a developmental learning perspective. A framework of a developmental swarm intelligence algorithm, which contains capacity developing stage and capability learning stage, is further given to help understand developmental swarm intelligence (DSI) algorithms, and to guide to design and/or implement any new developmental swarm intelligence algorithm and/or any developmental evolutionary algorithm. Following DSI, innovation is discussed and an innovation-inspired optimization (IO) algorithm is designed and developed. Finally, by combing the DSI and IO algorithm together, a unified swarm intelligence algorithm is proposed, which contains capacity developing stage and capability learning stage and with three search operators in its capability learning stage to mimic the three levels of innovations.

Soccer Game Optimization

2015 ◽  
pp. 1434-1469 ◽  
Author(s):  
Hindriyanto Dwi Purnomo ◽  
Hui-Ming Wee
Keyword(s):  
Evolutionary Algorithm ◽  
Information Sharing ◽  
Swarm Intelligence ◽  
Soccer Player ◽  
Optimization Problems ◽  
Optimization Method ◽  
New Method ◽  
Intelligence Algorithm ◽  
Reproduction Process ◽  
Swarm Intelligence Algorithm

A new metaheuristic algorithm is proposed. The algorithm integrates the information sharing as well as the evolution operators in the swarm intelligence algorithm and evolutionary algorithm respectively. The basic soccer player movement is used as the analogy to describe the algorithm. The new method has two basic operators; the move off and the move forward. The proposed method elaborates the reproduction process in evolutionary algorithm with the powerful information sharing in the swarm intelligence algorithm. Examples of implementations are provided for continuous and discrete problems. The experiment results reveal that the proposed method has the potential to become a powerful optimization method. As a new method, the proposed algorithm can be enhanced in many different ways such as investigating the parameter setting, elaborating more aspects of the soccer player movement as well as implementing the proposed method to solve various optimization problems.

Soccer Game Optimization

2013 ◽  
pp. 386-420 ◽  
Author(s):  
Hindriyanto Dwi Purnomo ◽  
Hui-Ming Wee
Keyword(s):  
Evolutionary Algorithm ◽  
Information Sharing ◽  
Swarm Intelligence ◽  
Soccer Player ◽  
Optimization Problems ◽  
Optimization Method ◽  
New Method ◽  
Intelligence Algorithm ◽  
Reproduction Process ◽  
Swarm Intelligence Algorithm

A new metaheuristic algorithm is proposed. The algorithm integrates the information sharing as well as the evolution operators in the swarm intelligence algorithm and evolutionary algorithm respectively. The basic soccer player movement is used as the analogy to describe the algorithm. The new method has two basic operators; the move off and the move forward. The proposed method elaborates the reproduction process in evolutionary algorithm with the powerful information sharing in the swarm intelligence algorithm. Examples of implementations are provided for continuous and discrete problems. The experiment results reveal that the proposed method has the potential to become a powerful optimization method. As a new method, the proposed algorithm can be enhanced in many different ways such as investigating the parameter setting, elaborating more aspects of the soccer player movement as well as implementing the proposed method to solve various optimization problems.

Research on Swarm Intelligence Algorithm with an Artificial Bee Colony Algorithm for Lot Streaming Problem in Job Shop

2014 ◽  
Vol 951 ◽  
pp. 239-244 ◽  
Author(s):  
Xiao Qiang Xu ◽  
De Ming Lei
Keyword(s):  
Swarm Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Job Shop ◽  
Lot Streaming ◽  
Lot Scheduling ◽  
Bee Colony ◽  
Intelligence Algorithm ◽  
Second Period ◽  
Swarm Intelligence Algorithm

The lot streaming (LS) problem in job shop with equal-size sub-lots and intermittent idling is considered. An effective swarm intelligence algorithm with an artificial bee colony (ABC) algorithm is proposed for the minimization of total penalties of tardiness and earliness. In the first period of ABC, the employed bee phase and the onlooker bee phase are both for lot/sub-lot scheduling. In the second period, the LS conditions are determined in the employed bee phase and the lot/sub-lot is scheduled in the onlooker phase. The worst solution of the swarm is replaced with the elite one every few cycles. Computational results show the promising advantage of ABC.

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