8,108 research outputs found
Multi-Guide Particle Swarm Optimization for Large-Scale Multi-Objective Optimization Problems
Multi-guide particle swarm optimization (MGPSO) is a novel metaheuristic for multi-objective optimization based on particle swarm optimization (PSO). MGPSO has been shown to be competitive when compared with other state-of-the-art multi-objective optimization algorithms for low-dimensional problems. However, to the best of the author’s knowledge, the suitability of MGPSO for high-dimensional multi-objective optimization problems has not been studied. One goal of this thesis is to provide a scalability study of MGPSO in order to evaluate its efficacy for high-dimensional multi-objective optimization problems. It is observed that while MGPSO has comparable performance to state-of-the-art multi-objective optimization algorithms, it experiences a performance drop with the increase in the problem dimensionality. Therefore, a main contribution of this work is a new scalable MGPSO-based algorithm, termed cooperative co-evolutionary multi-guide particle swarm optimization (CCMGPSO), that incorporates ideas from cooperative PSOs. A detailed empirical study on well-known benchmark problems comparing the proposed improved approach with various state-of-the-art multi-objective optimization algorithms is done. Results show that the proposed CCMGPSO is highly competitive for high-dimensional problems
Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization
We study the evolution of cooperation among selfish individuals in the
stochastic strategy spatial prisoner's dilemma game. We equip players with the
particle swarm optimization technique, and find that it may lead to highly
cooperative states even if the temptations to defect are strong. The concept of
particle swarm optimization was originally introduced within a simple model of
social dynamics that can describe the formation of a swarm, i.e., analogous to
a swarm of bees searching for a food source. Essentially, particle swarm
optimization foresees changes in the velocity profile of each player, such that
the best locations are targeted and eventually occupied. In our case, each
player keeps track of the highest payoff attained within a local topological
neighborhood and its individual highest payoff. Thus, players make use of their
own memory that keeps score of the most profitable strategy in previous
actions, as well as use of the knowledge gained by the swarm as a whole, to
find the best available strategy for themselves and the society. Following
extensive simulations of this setup, we find a significant increase in the
level of cooperation for a wide range of parameters, and also a full resolution
of the prisoner's dilemma. We also demonstrate extreme efficiency of the
optimization algorithm when dealing with environments that strongly favor the
proliferation of defection, which in turn suggests that swarming could be an
important phenomenon by means of which cooperation can be sustained even under
highly unfavorable conditions. We thus present an alternative way of
understanding the evolution of cooperative behavior and its ubiquitous presence
in nature, and we hope that this study will be inspirational for future efforts
aimed in this direction.Comment: 12 pages, 4 figures; accepted for publication in PLoS ON
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
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