Chaotic Cat Swarm Algorithms for Global Numerical Optimization

2012 ◽  
Vol 602-604 ◽  
pp. 1782-1786
Author(s):  
Shi Da Yang ◽  
Ya Lin Yi ◽  
Zhi Yong Shan
Keyword(s):  
Global Optimization ◽  
Numerical Optimization ◽  
Chaotic Maps ◽  
Heuristic Optimization ◽  
Global Numerical Optimization ◽  
Natural Behavior ◽  
Swarm Algorithms ◽  
Behavior Of Cats ◽  
Swarm Algorithm

A novel Chaotic Improved Cat Swarm Algorithm (CCSA) is presented for global optimization. The CSA is a new meta-heuristic optimization developed based on imitating the natural behavior of cats and composed of two sub-models: tracing mode and seeking mode, which model upon the behaviors of cats. Here different chaotic maps are utilized to improve the seeking mode step of the algorithm. Seven different chaotic maps are investigated and the Logistic and Sinusoidal maps are found as the best choices. Comparing the new algorithm with the CSA method demonstrates the superiority of the CCSA for the benchmark functions.

Chaotic Grenade Explosion Algorithms for Global Numerical Optimization

2012 ◽  
Vol 602-604 ◽  
pp. 1787-1792
Author(s):  
Shi Da Yang ◽  
Ya Lin Yi ◽  
Yan Ping Lu
Keyword(s):  
Global Optimization ◽  
Numerical Optimization ◽  
Chaotic Maps ◽  
Heuristic Optimization ◽  
Global Numerical Optimization

A novel Chaotic Grenade Explosion Algorithm (CGEA) is presented for global optimization. The GEA is a new meta-heuristic optimization developed based on the observation of a grenade explosion, in which the thrown pieces of shrapnel destruct the objects near the explosion location. Here different chaotic maps are utilized to improve solution search equation of the algorithm. Seven different chaotic maps are investigated. Comparing the new algorithm with the GEA demonstrates the superiority of the CGEA for the benchmark functions.

Salp swarm algorithm with crossover scheme and Lévy flight for global optimization

2021 ◽  
pp. 1-12
Author(s):  
Heming Jia ◽  
Chunbo Lang
Keyword(s):  
Global Optimization ◽  
Heuristic Algorithm ◽  
Experimental Results ◽  
High Dimensional ◽  
Lévy Flight ◽  
Test Functions ◽  
Levy Flight ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm ◽  
Accuracy And Stability

Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way.

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

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