Quantum-Behaved Particle Swarm Optimization with Novel Adaptive Strategies

Sheng, Xinyi; Xi, Maolong; Sun, Jun; Xu, Wenbo

doi:10.1260/1748-3018.9.2.143

Cited by 10 publications

(3 citation statements)

References 31 publications

(55 reference statements)

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“…Therefore, in order to maintain the best-fit chromosome and balance the global searching ability and convergence speed, elitist strategy was designed. 23 The advantage of an elitist strategy over traditional probabilistic reproduction is that the best solution is monotonically improving from one generation to the next. 24 The potential unfavorable factor is that it may result in an increase in the similarity of individuals and the problem of premature convergence.…”

Section: Elitist Strategymentioning

confidence: 99%

“…Due to the randomness of the GA, it is likely that the best individuals in the parent generation will no longer appear in new populations generated by their children chromosomes. Therefore, in order to maintain the best‐fit chromosome and balance the global searching ability and convergence speed, elitist strategy was designed 23 . The advantage of an elitist strategy over traditional probabilistic reproduction is that the best solution is monotonically improving from one generation to the next 24 .…”

Section: Proposal and Implementation Of The Improved Algorithmmentioning

confidence: 99%

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On‐chip evolution of combinational logic circuits using an improved genetic‐simulated annealing algorithm

Shang

Chen

Peng

2019

Concurrency and Computation

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This paper presents the on-chip evolution system of combinational logic circuits by a new hybrid algorithm known as improved genetic-simulated annealing algorithm (IGASA). IGASA is based on the concept and principles of genetic algorithm (GA) and simulated annealing (SA). The main idea is to combine GA's global search ability with the advantage of SA's fast convergence to reach an optimal solution. In this paper, a new mutation operation and a repeated annealing (RA) process are introduced to overcome the premature convergence of the standard GA and the large number of searches in the later period. An elitist strategy to enhance convergence characteristic of the proposed algorithm is incorporated to better speed up the evolution process. In addition, the new algorithm is implemented in a new evolvable platform using Intel Cyclone V FPGA with an embedded ARM microprocessor. Comparison of the performance of the improved algorithm to the pure GASA algorithm and standard GA is presented on a number of case studies. The experiment results demonstrate that feasible circuits are always achieved by the IGASA algorithm unlike with other algorithms and the number of generations required is less. KEYWORDS ARM processor, combinational logic circuits, evolvable hardware (EHW), field programmable system-on-chip (FPSoC), improved genetic-simulated annealing (IGASA) 1 INTRODUCTION Evolvable hardware (EHW) is a thriving area of research which uses the genetic algorithm (GA) to construct novel circuits without manual engineering. 1 It is also a new field at the confluence of reconfigurable devices, autonomous systems, artificial intelligence, and automatic design. 2 A complete hardware evolution system usually consists of two main elements: evolutionary algorithm (EA) 3 and reconfigurable hardware device. Specifically, EHW is generally classified into two major classes: extrinsic EHW 4 and intrinsic EHW. 5 The biggest difference between extrinsic EHW and intrinsic EHW is whether the chromosome is evaluated by software simulation or in hardware. In recent years, the emergence of Field Programmable System-on-Chips (FPSoCs) 6 has brought hardware evolution to a third dimension, called On-chip evolution. 7 The FPSoC implements the entire EHW system from a single chip by providing embedded processors (such as ARM cores). By constructing a virtual reconfigurable circuit (VRC) 8 layer, the problem of reconfiguration limitations of commercial FPGA can be well overcome. FPSoCs allow power consumption and board size to be reduced, and reliability and performance (thanks to on-chip communications) to be increased compared to external multichip solutions. 9 However, despite the potential advantages of the FPGA-ARM single-chip platform, the need for designers to have good knowledge and expertise in both hardware and software domains makes this area of research reported in very few works, 10 and all of those works only targeting Xilinx Zynq-7000 devices. 11 Therefore, in order to analyze the detailed performance of different FPSoCs that evolve ...

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Section: Elitist Strategymentioning

confidence: 99%

Section: Proposal and Implementation Of The Improved Algorithmmentioning

confidence: 99%

On‐chip evolution of combinational logic circuits using an improved genetic‐simulated annealing algorithm

Shang

Chen

Peng

2019

Concurrency and Computation

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“…Intelligent optimization algorithms can make the random forest method less likely to fall into a local optimum during the search process and find the global optimal solution with great probability. Particle swarm optimization (PSO) is a data mining optimization method that adjusts meaningful features from the input dataset [27]. However, PSO algorithms have limitations, such as low convergence rates, and tend to fall into local optimization in high-dimensional spaces.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Variation of Cyanobacteria Blooms in Taihu Lake Using Multiple Remote Sensing Indices and Machine Learning

Pan,

Yuan,

Yang

et al. 2024

Remote Sensing

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In view of the ecological threat posed by cyanobacteria blooms in Taihu Lake (China), this paper presents a study on the area of cyanobacteria extent based on MODIS data using the quantum particle swarm optimization–random forest (QPSO-RF) machine learning algorithm. This paper selects multiple remote sensing input indices that can represent the characteristics of the primary underlying type in Taihu Lake. The proposed method performs best, with an F1 score of 0.91–0.98. Based on this method, the spatio-temporal variation of cyanobacteria blooms in the Taihu Lake complex was analyzed. During 2010–2022, the average area of cyanobacteria blooms in Taihu Lake increased slightly. Severe-scale cyanobacteria blooms occurred in 2015–2019. Cyanobacteria blooms were normally concentrated from May to November. However, the most prolonged extended duration occurred in 2017, lasting for eight months. Spatially, cyanobacteria blooms were mainly identified in the northwestern part of Taihu Lake, with an average occurrence frequency of about 10.0%. The cyanobacteria blooms often began to grow in the northwestern part of the lake and then spread to the Center of the Lake, and also dissipated earliest in the northwestern part of the lake. Our study is also beneficial for monitoring the growth of cyanobacteria blooms in other similar large lakes in long time series.

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An ensemble algorithm based on adaptive chaotic quantum-behaved particle swarm optimization with weibull distribution and hunger games search and its financial application in parameter identification

Ye,

Dong

2024

Appl Intell

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Quantum-Behaved Particle Swarm Optimization with Novel Adaptive Strategies

Cited by 10 publications

References 31 publications

On‐chip evolution of combinational logic circuits using an improved genetic‐simulated annealing algorithm

On‐chip evolution of combinational logic circuits using an improved genetic‐simulated annealing algorithm

Spatio-Temporal Variation of Cyanobacteria Blooms in Taihu Lake Using Multiple Remote Sensing Indices and Machine Learning

An ensemble algorithm based on adaptive chaotic quantum-behaved particle swarm optimization with weibull distribution and hunger games search and its financial application in parameter identification

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