- 著者
- Tomoyuki Sasaki Hidehiro Nakano
- 出版者
- The Institute of Electronics, Information and Communication Engineers
- 雑誌
- Nonlinear Theory and Its Applications, IEICE (ISSN:21854106)
- 巻号頁・発行日
- vol.13, no.2, pp.170-195, 2022 (Released:2022-04-01)
- 参考文献数
- 37
Swarm intelligence (SI) algorithms have been studied in solving real-world optimization problems called black-box optimization problems. Typical features of SI algorithms are: (1) being a population-based metaheuristics; (2) using fitness values of a given objective function; and (3) having very simple search rules which search agents follow. As such, SI algorithms have been applied to various black-box optimization problems. Particle swarm optimization is one of powerful SI algorithms, in which a swarm consists of plural particles as solution candidates. Particles directly fly a search space and share their own information each other, and thus PSO can find good quality of solutions. However, a PSO swarm is easily stuck in solving optimization problems whose search space is high-dimensional and complicated. In order to solve such problems, large numbers of particles and reference frame invariance are needed for PSO algorithms. Herein, we suggest a piecewise-linear particle swarm optimizer (PPSO) which is a deterministic PSO. PPSO has two simple search modes switched to another mode dynamically, whose search dynamics are complex. As such, PPSO algorithm can be implemented on hardware with low hardware costs because PPSO algorithm must not require many random number generators. In addition, PPSO algorithm can find a good quality of solution in solving complex optimization problems. We studied search performances of PPSO compared to PSO algorithms and provide theoretical analysis of reference frame invariance for PPSO. In order to verify search performances and theoretical analysis, we performed numerical simulations.
- 著者
- Tomoyuki Sasaki Hidehiro Nakano
- 出版者
- The Institute of Electronics, Information and Communication Engineers
- 雑誌
- Nonlinear Theory and Its Applications, IEICE (ISSN:21854106)
- 巻号頁・発行日
- vol.14, no.2, pp.267-291, 2023 (Released:2023-04-01)
- 参考文献数
- 26
This paper relates to study on a new deterministic particle swarm optimization (D-PSO) called Optimizer based on Spiking Neural-oscillator Networks (OSNNs). OSNNs have a swarm consisting of plural particles which search a solution space interacting with each other. A single particle consists of plural spiking neural oscillators (`spiking oscillators') modeled by integrate-and-fire neurons. The spiking oscillators are coupled by a network topology and interact with each other by exchanging their own spike signals. Such interaction results in that coupling spiking oscillators can take synchronous or asynchronous dynamics and affects search performances of OSNNs. Herein we propose the basic algorithm of OSNNs and applied Ring 1-way network topology to coupling spiking oscillators. We theoretically analyzed parameter conditions for OSNNs, demonstrated the analytic results, and verified search performances of OSNNs through numerical simulations. We also herein discuss search performances of OSNNs and the relationship between the search performances and analytic results, and clarify prospective parameter regions which lead to good search performances in solving optimization problems.