When coping with complex global optimization problems, often it is not possible to obtain either analytical or exact solutions. Therefore, one is forced to resort to approximate numerical optimizers. With this aim, several metaheuristics have been proposed in the literature and the primary approaches can be traced back to biology and physics. On one hand, there exist bio-inspired metaheuristics that imitate the Darwinian evolution of species (like, for instance, Genetic Algorithms) or the behaviour of group of social organisms (like, for instance, Ant Colony Optimization). On the other hand, there exist physics-inspired metaheuristics that mimic physical laws (like, for instance, gravitation and electromagnetism). In this work, we take into...
In this paper, we propose and apply a methodology to improve the performances of trading systems bas...
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by ...
We compare the performance of six recent global optimization algorithms: Imperialist Competitive Alg...
When coping with complex global optimization problems, often it is not possible to obtain either ana...
In this paper we consider a simple trading system (TS) based on a set of Technical Analysis (TA) ind...
In recent years, several optimization methods especially metaheuristic optimization methods have bee...
The paper presents the results of comparison of three metaheuristics that currently exist in the pro...
Summarization: This paper discusses applications of nature-inspired techniques in optimisation probl...
International audienceThis work investigates the potential of the particle swarm algorithm for the o...
Abstract. Inspired by observing fireworks explosion, a novel swarm in-telligence algorithm, called F...
Collective Intelligence Systems draw their inspiration from biology where a number of simple units o...
The recent developments in science and technology make imperative the need for efficient and e_ectiv...
This work concerns the optimization of a Trading Systems (TS) based on a small set of Technical Anal...
Metaheuristic optimization algorithms (Nature-Inspired Optimization Algorithms) are a class of algor...
Particle Swarm Optimization (PSO) is an algorithm for swarm intelligence based on stochastic and pop...
In this paper, we propose and apply a methodology to improve the performances of trading systems bas...
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by ...
We compare the performance of six recent global optimization algorithms: Imperialist Competitive Alg...
When coping with complex global optimization problems, often it is not possible to obtain either ana...
In this paper we consider a simple trading system (TS) based on a set of Technical Analysis (TA) ind...
In recent years, several optimization methods especially metaheuristic optimization methods have bee...
The paper presents the results of comparison of three metaheuristics that currently exist in the pro...
Summarization: This paper discusses applications of nature-inspired techniques in optimisation probl...
International audienceThis work investigates the potential of the particle swarm algorithm for the o...
Abstract. Inspired by observing fireworks explosion, a novel swarm in-telligence algorithm, called F...
Collective Intelligence Systems draw their inspiration from biology where a number of simple units o...
The recent developments in science and technology make imperative the need for efficient and e_ectiv...
This work concerns the optimization of a Trading Systems (TS) based on a small set of Technical Anal...
Metaheuristic optimization algorithms (Nature-Inspired Optimization Algorithms) are a class of algor...
Particle Swarm Optimization (PSO) is an algorithm for swarm intelligence based on stochastic and pop...
In this paper, we propose and apply a methodology to improve the performances of trading systems bas...
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by ...
We compare the performance of six recent global optimization algorithms: Imperialist Competitive Alg...