engineering-optimisation
There are 0 repository under engineering-optimisation topic.
alimirjalili / GWO
The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented to perform optimization. This is the source codes of the paper: S. Mirjalili, S. M. Mirjalili, A. Lewis, Grey Wolf Optimizer, Advances in Engineering Software, Volume 69, March 2014, Pages 46-61, ISSN 0965-9978, http://dx.doi.org/10.1016/j.advengsoft.2013.12.007. More information can be found in: http://www.alimirjalili.com/GWO.html
Language:MATLAB 33ali-ece / Design-of-optimal-CMOS-ring-oscillator-using-an-intelligent-optimization-tool
This paper presents an intelligent sizing method to improve the performance and efficiency of a CMOS Ring Oscillator (RO). The proposed approach is based on the simultaneous utilization of powerful and new multi-objective optimization techniques along with a circuit simulator under a data link. The proposed optimizing tool creates a perfect tradeoff between the contradictory objective functions in CMOS RO optimal design. This tool is applied for intelligent estimation of the circuit parameters (channel width of transistors), which have a decisive influence on RO specifications. Along the optimal RO design in an specified range of oscillaton frequency, the Power Consumption, Phase Noise, Figure of Merit (FoM), Integration Index, Design Cycle Time are considered as objective functions. Also, in generation of Pareto front some important issues, i.e. Overall Nondominated Vector Generation (ONVG), and Spacing (S) are considered for more effectiveness of the obtained feasible solutions in application. Four optimization algorithms called Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Inclined Planes system Optimization (MOIPO), Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Modified Inclined Planes System Optimization (MOMIPO) are utilized for 0.18-mm CMOS technology with supply voltage of 1-V. Baesd on our extensive simulations and experimental results MOMIPO outperforms the best performance among other multi-objective algorithms in presented RO designing tool.
multi-objective-optimizationpareto-optimalitypareto-frontoptimizationoptimization-algorithmsswarm-intelligenceevolutionary-algorithmsevolutionary-computationengineering-optimisationbio-inspired-optimizationheuristic-search-algorithmsmetaheuristic-optimisationmetaheuristic-algorithmsvlsi-circuitshspicematlabA simplified and effective version of IPO (called SIPO) with the aim of simplifying the main IPO equations, creating a powerful trade-off between the concepts of exploitation and exploration, and modifying the complexity of their structural parameters.
bio-inspired-optimizationoptimization-algorithmssoft-computingsoft-computing-techniquesoptimizationengineering-optimisationintelligent-systemsartificial-intelligenceevolutionary-algorithmsevolutionary-computationswarm-intelligencematlabbenchmarksheuristic-search-algorithmsmetaheuristic-optimisationmetaheuristic-algorithmscomputer-aided-designLanguage:MATLAB 1Multi-objective optimization based on sloping plate optimization algorithm called Multi-objective Inclined Planes system optimization algorithm (MOIPO) is presented in this link. The proposed method uses the concept of Pareto optimization to identify non-dominant positions and an external tank to maintain these positions.
soft-computingartifical-intelligenseevolutionary-algorithmsmulti-objective-optimizationevolutionary-computationswarm-intelligenceoptimizationbio-inspired-optimizationoptimization-algorithmsengineering-optimisationmatlabhspiceheuristic-search-algorithmsmetaheuristic-optimisationmetaheuristic-algorithmsparticle-swarm-optimizationpareto-optimalitypareto-front0Fs02 / r-cuckoo-search
R implementation of "Engineering Optimisation by Cuckoo Search"
Language:R