scholarly journals Multiobjective optimization for small meander wire dipole antennas in a fixed area using ant colony system

2009 ◽  
Vol 19 (5) ◽  
pp. 592-597 ◽  
Author(s):  
Amir Galehdar ◽  
David V. Thiel ◽  
Andrew Lewis ◽  
Marcus Randall
Keyword(s):  
Multiobjective Optimization ◽  
Ant Colony ◽  
Ant Colony System ◽  
Dipole Antennas ◽  
Small Meander ◽  
Fixed Area

scholarly journals Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110192
Author(s):  
Songcan Zhang ◽  
Jiexin Pu ◽  
Yanna Si ◽  
Lifan Sun
Keyword(s):  
Path Planning ◽  
Convergence Rate ◽  
Mobile Robot ◽  
Local Optimization ◽  
Ant Colony ◽  
Ant Colony System ◽  
Geometric Features ◽  
Robot Path Planning ◽  
Computation Efficiency ◽  
Robot Path

Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms.

scholarly journals Suspicious Activity Recognition Using Proposed Deep L4-Branched-ActionNet with Entropy Coded Ant Colony System Optimization

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Tanzila Saba ◽  
Amjad Rehman ◽  
Rabia Latif ◽  
Suliman Mohamed Fati ◽  
Mudassar Raza ◽  
...  
Keyword(s):  
Activity Recognition ◽  
System Optimization ◽  
Ant Colony ◽  
Ant Colony System ◽  
Suspicious Activity

scholarly journals A Novel Bioinspired Multiobjective Optimization Algorithm for Designing Wireless Sensor Networks in the Internet of Things

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Jun Huang ◽  
Liqian Xu ◽  
Cong-cong Xing ◽  
Qiang Duan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Chaotic Behavior ◽  
Ant Colony ◽  
Wireless Sensor ◽  
The Internet ◽  
The Internet Of Things

The design of wireless sensor networks (WSNs) in the Internet of Things (IoT) faces many new challenges that must be addressed through an optimization of multiple design objectives. Therefore, multiobjective optimization is an important research topic in this field. In this paper, we develop a new efficient multiobjective optimization algorithm based on the chaotic ant swarm (CAS). Unlike the ant colony optimization (ACO) algorithm, CAS takes advantage of both the chaotic behavior of a single ant and the self-organization behavior of the ant colony. We first describe the CAS and its nonlinear dynamic model and then extend it to a multiobjective optimizer. Specifically, we first adopt the concepts of “nondominated sorting” and “crowding distance” to allow the algorithm to obtain the true or near optimum. Next, we redefine the rule of “neighbor” selection for each individual (ant) to enable the algorithm to converge and to distribute the solutions evenly. Also, we collect the current best individuals within each generation and employ the “archive-based” approach to expedite the convergence of the algorithm. The numerical experiments show that the proposed algorithm outperforms two leading algorithms on most well-known test instances in terms of Generational Distance, Error Ratio, and Spacing.

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