High-Efficiency Multiobjective Synchronous Modeling and Solution of Analog ICs

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

“…Improvement of photoelectric performance of thin film solar cells [184] Optimization of nanosecond laser processing [185] VLSI floor planning optimization regarding measures such as area, wire length and dead space between modules [186] Lifetime reliability, performance and power consumption of heterogeneous multiprocessor embedded systems [187] MO Particle Swarm Optimization Review of many applications of MO PSO in diverse areas [188] Floor planning of the VLSI circuit and layout area minimization using MO PSO [189] MO Ant Colony Optimization A 3D printed bandpass frequency-selective surface structure with desired center frequency and bandwidth [190] Analog filter design [191] Multi-criteria optimization for VLSI floor planning [192] Artificial Bee Colony Area and power optimization for logic circuit design [193] Design of digital filters [194] Artificial Immune System Spectrum management and design of 6G networks [195] Multi-objective design of an inductor for a DC-DC buck converter [196] Differential Evolution Geometry optimization of high-index dielectric nanostructures [197] Multi-objective synchronous modeling and optimal solving of an analog IC [198] Firefly Algorithm Reducing heat generation, sizing and interconnect length for VLSI floor planning [199] Secure routing for fog-based wireless sensor networks [200] Cuckoo Search Multi-objective-derived energy-efficient routing in wireless sensor networks [201] Parameter extraction of photovoltaic cell based on a multi-objective approach [202] MO Grey Wolf Optimizer Electrochemical micro-drilling in MEMS [203] Multi-objective task scheduling in cloud-fog computing [204] Besides using metaheuristic algorithms, multi-objective optimization can be implemented using machine learning techniques such as artificial neural networks (multi-layer perceptrons), convolutional neural networks and recurrent neural networks.…”