Search results for "Premature convergence"
showing 6 items of 6 documents
Improving estimation of distribution genetic programming with novelty initialization
2021
Estimation of distribution genetic programming (EDA-GP) replaces the standard variation operations of genetic programming (GP) by learning and sampling from a probabilistic model. Unfortunately, many EDA-GP approaches suffer from a rapidly decreasing population diversity which often leads to premature convergence. However, novelty search, an approach that searches for novel solutions to cover sparse areas of the search space, can be used for generating diverse initial populations. In this work, we propose novelty initialization and test this new method on a generalization of the royal tree problem and compare its performance to ramped half-and-half (RHH) using a recent EDA-GP approach. We f…
Disturbed Exploitation compact Differential Evolution for Limited Memory Optimization Problems
2011
This paper proposes a novel and unconventional Memetic Computing approach for solving continuous optimization problems characterized by memory limitations. The proposed algorithm, unlike employing an explorative evolutionary framework and a set of local search algorithms, employs multiple exploitative search within the main framework and performs a multiple step global search by means of a randomized perturbation of the virtual population corresponding to a periodical randomization of the search for the exploitative operators. The proposed Memetic Computing approach is based on a populationless (compact) evolutionary framework which, instead of processing a population of solutions, handles …
Passive congregation based particle swam optimization (pso) with self-organizing hierarchical approach for non-convex economic dispatch
2017
This paper proposes a passive congregation based PSO with self-organizing hierarchical algorithm approach for solving the economic dispatch problem of power system, where some of the units have prohibited operating zones. This Algorithm is known to perform better than conventional gradient based optimization methods for non-convex optimization problems. Conventional PSO algorithm is a population based heuristic search, employing problem of premature convergence. In this work, an innovative approach based on the concept of passive congregation based PSO with self-organizing hierarchical approach is employed to overcome the problem of premature convergence in classical PSO method.
An optimization location scheme for electric charging stations
2013
International audience; Due to environmental issues, electric mobility is one of the mobility alternatives that are receiving a huge attention nowadays. In fact, in the last few years electric vehicles have entered the world's car market. This revolutionary technology requires a fast deployment of electric charging stations since the key issue in this system is recharging the batteries. In this work, we propose an optimized algorithm to locate electric-vehicles charging stations. Different factors and limitations are considered and a real case study is given as an application. We first determine the appropriate strict constraints and cost of charging stations' location; and then we propose …
An adaptive multimeme algorithm for designing HIV multidrug therapies.
2007
This paper proposes a period representation for modeling the multidrug HIV therapies and an Adaptive Multimeme Algorithm (AMmA) for designing the optimal therapy. The period representation offers benefits in terms of flexibility and reduction in dimensionality compared to the binary representation. The AMmA is a memetic algorithm which employs a list of three local searchers adaptively activated by an evolutionary framework. These local searchers, having different features according to the exploration logic and the pivot rule, have the role of exploring the decision space from different and complementary perspectives and, thus, assisting the standard evolutionary operators in the optimizati…
Diversity Management in Memetic Algorithms
2012
In Evolutionary Computing, Swarm Intelligence, and more generally, populationbased algorithms diversity plays a crucial role in the success of the optimization. Diversity is a property of a group of individuals which indicates how much these individuals are alike. Clearly, a group composed of individuals similar to each other is said to have a low diversity whilst a group of individuals dissimilar to each other is said to have a high diversity. In computer science, in the context of population-based algorithms the concept of diversity is more specific: the diversity of a population is a measure of the number of different solutions present, see [239].