Percorrer por autor "Merelo, J. J."

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    Particle swarm and population structure
    (ACM, 2018) Fernandes, Carlos M.; Rosa, Agostinho C.; Fachada, Nuno; Laredo, Juan L. J.; Merelo, J. J.; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
    We investigate the convergence speed, accuracy, robustness and scalability of PSOs structured by regular and random graphs with 3 ≤ k ≤ n. The main conclusion is that regular and random graphs with the same averaged connectivity k may result in significantly different performance, namely when k is low.
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    Population sizing of cellular evolutionary algorithms
    (Elsevier, 2020) Fernandes, Carlos M.; Fachada, Nuno; Laredo, Juan L. J.; Merelo, J. J.; Rosa, Agostinho C.; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
    Cellular evolutionary algorithms (cEAs) are a particular type of EAs in which a communication structure is imposed to the population and mating restricted to topographically nearby individuals. In general, these algorithms have longer takeover times than panmictic EAs and previous investigations argue that they are more efficient in escaping local optima of multimodal and deceptive functions. However, most of those studies are not primarily concerned with population size, despite being one of the design decisions with a greater impact in the accuracy and convergence speed of population-based metaheuristics. In this paper, optimal population size for cEAs structured by regular and random graphs with different degree is estimated. Selecto-recombinative cEAs and standard cEAs with mutation and different types of crossover were tested on a class of functions with tunable degrees of difficulty. Results and statistical tests demonstrate the importance of setting an appropriate population size. Event Takeover Values (ETV) were also studied and previous assumptions on their distribution were not confirmed: although ETV distributions of panmictic EAs are heavy-tailed, log-log plots of complementary cumulative distribution functions display no linearity. Furthermore, statistical tests on ETVs generated by several instances of the problems conclude that power law models cannot be favored over log-normal. On the other hand, results confirm that cEAs impose deviations to distribution tails and that large ETVs are less probable when the population is structured by graphs with low connectivity degree. Finally, results suggest that for panmictic EAs the ETVs’ upper bounds are approximately equal to the optimal population size. Keywords: Spatially structured evolutionary algorithms; Cellular evolutionary algorithms;Optimal population size; Event takeover values
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    Revisiting Population Structure and Particle Swarm Performance
    (SciTePress, Science and Technology Publications, 2018) Fernandes, Carlos M.; Fachada, Nuno; Laredo, Juan L. J.; Merelo, J. J.; Castillo, P. A.; Rosa, Agostinho C.; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
    Population structure strongly affects the dynamic behavior and performance of the particle swarm optimization (PSO) algorithm. Most of PSOs use one of two simple sociometric principles for defining the structure. One connects all the members of the swarm to one another. This strategy is often called gbest and results in a connectivity degree k = n, where n is the population size. The other connects the population in a ring with k = 3. Between these upper and lower bounds there are a vast number of strategies that can be explored for enhancing the performance and adaptability of the algorithm. This paper investigates the convergence speed, accuracy, robustness and scalability of PSOs structured by regular and random graphs with 3≤k≤n. The main conclusion is that regular and random graphs with the same averaged connectivity k may result in significantly different performance, namely when k is low.
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    Steady state particle swarm
    (PeerJ Inc., 2019) Fernandes, Carlos M.; Fachada, Nuno; Merelo, J. J.; Rosa, Agostinho C.; Escola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
    This paper investigates the performance and scalability of a new update strategy for the particle swarm optimization (PSO) algorithm. The strategy is inspired by the Bak–Sneppen model of co-evolution between interacting species, which is basically a network of fitness values (representing species) that change over time according to a simple rule: the least fit species and its neighbors are iteratively replaced with random values. Following these guidelines, a steady state and dynamic update strategy for PSO algorithms is proposed: only the least fit particle and its neighbors are updated and evaluated in each time-step; the remaining particles maintain the same position and fitness, unless they meet the update criterion. The steady state PSO was tested on a set of unimodal, multimodal, noisy and rotated benchmark functions, significantly improving the quality of results and convergence speed of the standard PSOs and more sophisticated PSOs with dynamic parameters and neighborhood. A sensitivity analysis of the parameters confirms the performance enhancement with different parameter settings and scalability tests show that the algorithm behavior is consistent throughout a substantial range of solution vector dimensions.