An Entropy-Assisted Particle Swarm Optimizer for Large-Scale Optimization Problem

Abstract

Diversity maintenance is crucial for particle swarm optimizer’s (PSO) performance. However, the update mechanism for particles in the conventional PSO is poor in the performance of diversity maintenance, which usually results in a premature convergence or a stagnation of exploration in the searching space. To help particle swarm optimization enhance the ability in diversity maintenance, many works have proposed to adjust the distances among particles. However, such operators will result in a situation where the diversity maintenance and fitness evaluation are conducted in the same distance-based space. Therefore, it also brings a new challenge in trade-off between convergence speed and diversity preserving. In this paper, a novel PSO is proposed that employs competitive strategy and entropy measurement to manage convergence operator and diversity maintenance respectively. The proposed algorithm was applied to the large-scale optimization benchmark suite on CEC 2013 and the results demonstrate the proposed algorithm is feasible and competitive to address large scale optimization problems.

Developing a New Robust Swarm-Based Algorithm for Robot Analysis

“…The algorithm is initiated with sub-swarms of small size neighborhoods, slowly expanding through every iteration, absorbing other particles, taking advantage of both the global and local neighborhood structures to increase the performance of the PSO algorithm. Competitive strategy was used in [30] to manage convergence, while entropy measurement was employed to maintain the diversity of the swarm. In [31], an adaptive multi-swarm competition PSO was proposed where the swarm is adaptively divided into sub-swarms and a competition mechanism is used to maintain diversity in the swarms.…”

Cross-Entropy as a Metric for the Robustness of Drone Swarms

“…Cross-entropy is a measure of a discrepancy between two probability distributions [ 4 ]. It is used widely beyond the theory of information, e.g., as an objective function for the optimisation of traffic flow [ 11 ] or in a particle swarm optimisation [ 12 , 13 ]. It is also used in machine learning as a loss function for the training set of neural networks [ 14 ] or to improve the clustering of data [ 15 ].…”

“…Particle swarm optimisation (PSO) [ 34 ] can use entropy for the simulated set of states (“particles”) (EA-PSO) [ 12 ], and then it may apply cross-entropy in the meta-optimisation of the search space. Various modifications and extensions exist, such as memetic based [ 13 ], niche strategy [ 35 ], or clustering [ 36 ].…”

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

“…Hence, solutions move and interact, rather than being evolved as in EAs, according to the dynamics outlined in Reference [25]. Several PSO variants have been designed to deal with a wide range of problems [26], including large scale ones [27,28], as well as challenging engineering applications [29], and hybrid versions were also designed thus generating effective PSO based multi-strategy approaches [30] and Estimation of Distribution Algorithms (EDAs) [31,32]. The EDA framework is quite interesting and has proven to be successful over different fields such as Robotics [33] and combinatorial domains [21].…”