Multi-Swarm Multi-Objective Optimizer Based on p-Optimality Criteria for Multi-Objective Portfolio Management
نویسندگان
چکیده
منابع مشابه
Dynamic Sensor Management Using Multi Objective Particle Swarm Optimizer
This paper presents a Swarm Intelligence based approach for sensor management of a multi sensor networks. Alternate sensor configurations and fusion strategies are evaluated by swarm agents, and an optimum configuration and fusion strategy evolves. An evolutionary algorithm, particle swarm optimization, is modified to optimize two objectives: accuracy and time. The output of the algorithm is th...
متن کاملA Particle Swarm Optimizer for Multi-Objective Optimization
This paper proposes a hybrid particle swarm approach called Simple Multi-Objective Particle Swarm Optimizer (SMOPSO) which incorporates Pareto dominance, an elitist policy, and two techniques to maintain diversity: a mutation operator and a grid which is used as a geographical location over objective function space. In order to validate our approach we use three well-known test functions propos...
متن کاملTowards a More Efficient Multi-Objective Particle Swarm Optimizer
AbstrAct This chapter presents a hybrid between a particle swarm optimization (PSO) approach and scatter search. The main motivation for developing this approach is to combine the high convergence rate of the PSO algorithm with a local search approach based on scatter search, in order to have the main advantages of these two types of techniques. We propose a new leader selection scheme for PSO,...
متن کاملA Multi-objective Particle Swarm Optimizer Hybridized with Scatter Search
This paper presents a new multi-objective evolutionary algorithm which consists of a hybrid between a particle swarm optimization (PSO) approach and scatter search. The main idea of the approach is to combine the high convergence rate of the particle swarm optimization algorithm with a local search approach based on scatter search. We propose a new leader selection scheme for PSO, which aims to...
متن کاملA scalable coevolutionary multi-objective particle swarm optimizer
Multi-Objective Particle Swarm Optimizers (MOPSOs) are easily trapped in local optima, cost more function evaluations and suffer from the curse of dimensionality. A scalable cooperative coevolution and -dominance based MOPSO (CEPSO) is proposed to address these issues. In CEPSO, Multi-objective Optimization Problems (MOPs) are decomposed in terms of their decision variables and are optimized b...
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: Mathematical Problems in Engineering
سال: 2019
ISSN: 1024-123X,1563-5147
DOI: 10.1155/2019/8418369