Comparison of Genetic Algorithms and Particle Swarm Optimisation for Fermentation Feed Profile Determination
نویسنده
چکیده
In recent years the area of Evolutionary Computation has come into its own. Two of the popular developed approaches are Genetic Algorithms and Particle Swarm Optimisation, both of which are used in optimisation problems. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their implementation. A study is presented illustrating the performance of both genetic algorithms and particle swarm optimisation, demonstrating their ability to generate a fermentation process feed profile based on a number of objective functions. Results demonstrate how the learning mechanism developed an optimal feed profile which meets the defined criteria.
منابع مشابه
An Energy Efficient Control Strategy for Induction Machines Based on Advanced Particle Swarm Optimisation Algorithms
This paper proposes an energy efficient control strategy for an induction machine (IM) based on two advanced particle swarm optimisation (PSO) algorithms. Two advanced PSO algorithms, known as the dynamic particle swarm optimisation (Dynamic PSO) and the chaos particle swarm optimisation (Chaos PSO) algorithms modify the algorithm parameters to improve the performance of the standard PSO algori...
متن کاملResearch of Blind Signals Separation with Genetic Algorithm and Particle Swarm Optimization Based on Mutual Information
Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...
متن کاملResearch of Blind Signals Separation with Genetic Algorithm and Particle Swarm Optimization Based on Mutual Information
Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...
متن کاملComparative Study of Particle Swarm Optimization and Genetic Algorithm Applied for Noisy Non-Linear Optimization Problems
Optimization of noisy non-linear problems plays a key role in engineering and design problems. These optimization problems can't be solved effectively by using conventional optimization methods. However, metaheuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) seem very efficient to approach in these problems and became very popular. The efficiency of these ...
متن کاملEstimation of Moisture in Transformer Insulation Using Dielectric Frequency Response Analysis by Heuristic Algorithms
Transformers are one of the most valuable assets of power systems. Maintenance and condition assessment of transformers has become one of the concerns of researchers due to huge number of transformers has been approached to the end of their lifetimes. Transformer’s lifetime depends on the life of its insulation and the insulation’s life is strongly influenced by its moisture attraction as well....
متن کامل