Complex-valued Encoding Research Articles

Multicore Photonic Complex-Valued Neural Network with Transformation Layer

Photonic neural network chips have been widely studied because of their low power consumption, high speed and large bandwidth. Using amplitude and phase to encode, photonic chips can accelerate complex-valued neural network computations. In this article, a photonic complex-valued neural network (PCNN) chip is designed. The scale of the single-core PCNN chip is limited because of optical losses, and the multicore architecture of the chip is used to improve computing capability. Further, for improving the performance of the PCNN, we propose the transformation layer, which can be implemented by the designed photonic chip to transform real-valued encoding to complex-valued encoding, which has richer information. Compared with real-valued input, the transformation layer can effectively improve the classification accuracy from 93.14% to 97.51% of a 64-dimensional input on the MNIST test set. Finally, we analyze the multicore computation of the PCNN. Compared with the single-core architecture, the multicore architecture can improve the classification accuracy by implementing larger neural networks and has better phase noise robustness. The proposed architecture and algorithms are beneficial to promote the accelerated computing of photonic chips for complex-valued neural networks and are promising for use in many applications, such as image recognition and signal processing.

A Complex-Valued Encoding Multichain Seeker Optimization Algorithm for Engineering Problems

This article comes up with a complex-valued encoding multichain seeker optimization algorithm (CMSOA) for the engineering optimization problems. The complex-valued encoding strategy and the multichain strategy are leaded in the seeker optimization algorithm (SOA). These strategies enhance the individuals’ diversity, enhance the local search, avert falling into the local optimum, and are the influential global optimization strategies. This article chooses fifteen benchmark functions, four proportional integral derivative (PID) control parameter models, and six constrained engineering problems to test. According to the experimental results, the CMSOA can be used in the benchmark functions, in the PID control parameter optimization, and in the optimization of constrained engineering problems. Compared to the particle swarm optimization (PSO), simulated annealing based on genetic algorithm (SA_GA), gravitational search algorithm (GSA), sine cosine algorithm (SCA), multiverse optimizer (MVO), and seeker optimization algorithm (SOA), the optimization ability and robustness of the CMSOA are better than those of others algorithms.

A Complex-valued Encoding Seeker Optimization Algorithm for Constrained Engineering Problems

This article comes up with a complex-valued encoding seeker optimization algorithm (CSOA) base on the multi-chain method for the constrained engineering optimization problems. The complex value encoding and a multi-link strategy are leaded by the seeker optimization algorithm (SOA). The complex value encoding method is an influential global optimization strategy, and the multi-link is an enhanced local search strategy. These strategies enhance the individuals’ diversity and avert fall into the local optimum. This article chose fifteen benchmark functions, four PID control parameter models, and six constrained engineering problems to test. According to the experimental results, the CSOA algorithm can be used in the benchmark functions, PID control parameters optimization, and optimization constrained engineering problems. Compared to particle swarm optimization (PSO), simulated annealing base on genetic algorithm (SA_GA), gravitational search algorithm (GSA), sine cosine algorithm (SCA), multi-verse optimizer (MVO), and seeker optimization algorithm (SOA), the optimization ability and robustness of CSOA are better.

Complex-valued encoding metaheuristic optimization algorithm: A comprehensive survey

The number of publications related to complex-valued encoding metaheuristic optimization research is increasing the area of metaheuristic optimization is gaining in popularity. In this paper, we aim to provide researchers with a comprehensive and extensive overview of complex-valued encoding metaheuristic algorithms and applications for function optimization, engineering optimization design, and combination optimization. Compared with the basic metaheuristic algorithm, which are based on real-valued encoding or binary encoding, the complex-valued encoding metaheuristic algorithm expands the dimension of the search region and efficiently avoids the problem of falling into the local minimum. Finally, eight complex-valued encoding metaheuristic algorithms were used for 29 benchmark test functions and five engineering optimization design problems. Through the analysis and comparison of the results with statistical significance, the superiority of complex-value encoding was proved, and the complex-value encoding metaheuristic algorithm with the best performance was obtained. The purpose of this review is to present a relatively comprehensive list of all the complex-value encoding metaheuristic algorithms in the literature to inspire further research.

Coordinated Complex-Valued Encoding Dragonfly Algorithm and Artificial Emotional Reinforcement Learning for Coordinated Secondary Voltage Control and Automatic Voltage Regulation in Multi-Generator Power Systems

This article proposes a coordinated optimization and control algorithm for coordinated secondary voltage control (CSVC) in multi-generator power systems. Firstly, to obtain a smaller voltage deviation and avoid the curse of dimensionality simultaneously, an artificial emotional reinforcement learning (AERL) is applied to automatic voltage regulation (AVR). Secondly, to obtain a smaller fitness value with lesser random for the decentralized independent variables optimization problem of the CSVC, a complex-valued encoding dragonfly algorithm (CDA) is proposed. Thirdly, the CDA and the AERL are coordinated for the CSVC and the AVR in multi-generator power systems. To verify the control performance of the AERL and the convergence of the proposed CDA, three simulation cases, i.e., IEEE 57-bus, 118-bus and 300-bus systems, are considered. The simulation results show that the CDA-AERL effectively obtains the smallest control objectives and the convergence for the CSVC in multi-generator power systems.

A Novel Complex-Valued Encoding Grey Wolf Optimization Algorithm

Grey wolf optimization (GWO) is one of the recently proposed heuristic algorithms imitating the leadership hierarchy and hunting mechanism of grey wolves in nature. The aim of these algorithms is to perform global optimization. This paper presents a modified GWO algorithm based on complex-valued encoding; namely the complex-valued encoding grey wolf optimization (CGWO). We use CGWO to test 16 unconstrained benchmark functions with seven different scales and infinite impulse response (IIR) model identification. Compared to the real-valued GWO algorithm and other optimization algorithms; the CGWO performs significantly better in terms of accuracy; robustness; and convergence speed.

A Complex-valued Encoding Bat Algorithm for Solving 0–1 Knapsack Problem

This paper proposes a novel complex-valued encoding bat algorithm (CPBA) for solving 0---1 knapsack problem. The complex-valued encoding method which can be considered as an efficient global optimization strategy is introduced to the bat algorithm. Based on the two-dimensional properties of the complex number, the real and imaginary parts of complex number are updated separately. The proposed algorithm can effectively diversify bat population and improving the convergence performance. The CPBA enhances exploration ability and is effective for solving both small-scale and large-scale 0---1 knapsack problem. Finally, numerical simulation is carried out, and the comparison results with some existing algorithms demonstrate the validity and stability of the proposed algorithm.

A Novel Complex Valued Cuckoo Search Algorithm

To expand the information of nest individuals, the idea of complex-valued encoding is used in cuckoo search (PCS); the gene of individuals is denoted by plurality, so a diploid swarm is structured by a sequence plurality. The value of independent variables for objective function is determined by modules, and a sign of them is determined by angles. The position of nest is divided into two parts, namely, real part gene and imaginary gene. The updating relation of complex-valued swarm is presented. Six typical functions are tested. The results are compared with cuckoo search based on real-valued encoding; the usefulness of the proposed algorithm is verified.

Polarimetric Calibration Using a Genetic Algorithm

This letter presents a novel calibration approach to polarimetric synthetic aperture radar using distributed targets with known scattering characteristics. A genetic algorithm with complex-valued encoding is proposed to solve the overdetermined system of nonlinear equations associated with the polarimetric calibration problem. The proposed genetic algorithm is described in detail, and validation of the algorithm is performed by numerical simulations.

Fundamental properties of spatial light modulators for the approximate optical computation of Fourier transforms: a review

The performance of optical computers that include program- mable Fourier optics depends intimately both on the physical character- istics of the particular spatial light modulator (SLM) and on the particular algorithms that map the ideal signal into the available modulation range of the SLM. Since practical affordable SLMs represent only a limited range of values in the complex plane (e.g., phase-only or quantized phase), numerous approaches have been reported to represent, ap- proximate, encode or map complex values onto the available SLM states. The best approach depends on the space-bandwidth product (SBWP) of the signal, number of SLM pixels, computation time of encod- ing, the required response time of the application, and the resulting per- formance of the optical computer. My review of various methods, as applied to most current SLMs, which have a relatively low number of high cost pixels, leads me to recommend encoding algorithms that address the entire usable frequency plane and that emphasize the fidelity of the approximated Fourier transform over maximization of diffraction effi- ciency and minimization of approximation error. Frequency-dependent diffraction efficiency (due to pixel fill factor of discrete SLMs or resolution of spatially continuous SLMs) is also evaluated as a factor that can limit usable SBWP and possibly modify the choice of encoding method. © 2001 Society of Photo-Optical Instrumentation Engineers. (DOI: 10.1117/1.1409336) Subject terms: spatial light modulators, computer generated holography, complex-valued encoding, statistical optics.