Observation of Bargaining Game using Co-evolution between Particle Swarm Optimization and Differential Evolution
نویسندگان
چکیده
منابع مشابه
Well Placement Optimization Using Differential Evolution Algorithm
Determining the optimal location of wells with the aid of an automated search algorithm is a significant and difficult step in the reservoir development process. It is a computationally intensive task due to the large number of simulation runs required. Therefore,the key issue to such automatic optimization is development of algorithms that can find acceptable solutions with a minimum numbe...
متن کاملMULTI-OBJECTIVE OPTIMIZATION OF ARCH DAMS USING DIFFERENTIAL EVOLUTION METHODS
For optimization of real-world arch dams, it is unavoidable to consider two or more conflicting objectives. This paper employs two multi-objective differential evolution algorithms (MoDE) in combination of a parallel working MATLAB-APDL code to obtain a set of Pareto solutions for optimal shape of arch dams. Full dam-reservoir interaction subjected to seismic loading is considered. A benchmark ...
متن کاملGaussian Particle Swarm Optimization with Differential Evolution Mutation
During the past decade, the particle swarm optimization (PSO) with various versions showed competitiveness on the constrained optimization problems. In this paper, an improved Gaussian particle swarm optimization algorithm (GPSO) is proposed to improve the diversity and local search ability of the population. A mutation operator based on differential evolution (DE) is designed and employed to u...
متن کاملEvolving Neural Networks: A Comparison between Differential Evolution and Particle Swarm Optimization
Due to their efficiency and adaptability, bio-inspired algorithms have shown their usefulness in a wide range of different non-linear optimization problems. In this paper, we compare two ways of training an artificial neural network (ANN): Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms. The main contribution of this paper is to show which of these two algorithms pr...
متن کاملExploratory Analysis of Clustering Problems Using a Comparison of Particle Swarm Optimization and Differential Evolution
Two modern optimization methods including Particle Swarm Optimization and Differential Evolution are compared on twelve constrained nonlinear test functions. Generally, the results show that Differential Evolution is better than Particle Swarm Optimization in terms of high-quality solutions, running time and robustness.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Journal of the Korea Contents Association
سال: 2014
ISSN: 1598-4877
DOI: 10.5392/jkca.2014.14.11.549