A Hybrid of Modified PSO and Local Search on a Multi-Robot Search System

Abstract

Particle swarm optimization (PSO), a new populationbased algorithm, has recently been used on multi-robot systems. Although this algorithm is applied to solve many optimization problems as well as multi-robot systems, it has some drawbacks when it is applied on multi-robot search systems to find a target in a search space containing big static obstacles. One of these defects is premature convergence. This means that one of the properties of basic PSO is that when particles are spread in a search space, as time increases they tend to converge in a small area. This shortcoming is also evident on a multi-robot search system, particularly when there are big static obstacles in the search space that prevent the robots from finding the target easily; therefore, as time increases, based on this property they converge to a small area that may not contain the target and become entrapped in that area. Another shortcoming is that basic PSO cannot guarantee the global convergence of the algorithm. In other words, initially particles explore different areas, but in some cases they are not good at exploiting promising areas, which will increase the search time.This study proposes a method based on the particle swarm optimization (PSO) technique on a multi-robot system to find a target in a search space containing big static obstacles. This method is not only able to overcome the premature convergence problem but also establishes an efficient balance between exploration and exploitation and guarantees global convergence, reducing the search time by combining with a local search method, such as A-star.To validate the effectiveness and usefulness of algorithms, a simulation environment has been developed for conducting simulation-based experiments in different scenarios and for reporting experimental results. These experimental results have demonstrated that the proposed method is able to overcome the premature convergence problem and guarantee global convergence.

Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers

“…Hyperopt Library is one of the libraries offering different hyper-optimization algorithms for machine learning algorithms [59]. Existing techniques for optimizing EC-based hyperparameters [60,61] such as differential evolution (DE) and particle swarm optimization (PSO) are useful since they are conceptually easy and can achieve highly competitive output in various fields [62][63][64][65]. However, these methods have a great deal of calculation and a low convergence rate in the iterative process.…”

“…The derived probabilities shown in Table 1 can be used to check the accuracy and efficiency of that particular rule. We adopted exclusive coverage in our implementation at the upper level such as unordered CN2 [62], whereas Laplace estimation was used for function evaluation at the lower level. Pre-pruning of rules was performed using two methods: (i) likelihood ratio statistic (LRS) tests, and (ii) minimum threshold for coverage of rules.…”

Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors

“…PSO was originally applied to continuous problems and then extended to discrete problems (Kennedy & Eberhart, 1997). Due to its simplicity and effectiveness, this algorithm is used inn different domains such as robotics (Couceiro, Rocha, & Ferreira, 2011;Nakisa, Nazri, Rastgoo, & Abdullah, 2014;Nakisa, Rastgoo, Nasrudin, & Nazri, 2014;Rastgoo, Nakisa, & Nazri, 2015) and job scheduling (Sha & Hsu, 2006;G. Zhang, Shao, Li, & Gao, 2009).…”

When Robots contribute to eradicate the COVID-19 spread in a context of containment

“…On the one hand, we have MSL-PSO (Multi-Swarm PSO with LS) which is a hybrid of modified Particle Swarm Optimization and Local Search on a Multirobot Search System [14], A-RPSO (Adaptive Robotic Particle Swarm Optimization) [3] that provides an adaptive inertia weight to avoid local optima in addition to the obstacle avoidance strategy of the RPSO [15], and MFSO (Multi-swarm hybrid FOA-PSO) [16]. However, all these approaches have only been applied for unique target search in completely static environments.…”

“…The positions being on 2 Dimensions, the equations are applied on the two components (respectively x-coordinate and y-coordinate) of each position. Each robot calculates its next position by Equation (14), which is originally based on Equation 10:…”