Particle swarm optimization (PSO) algorithm is a population-based stochastic technique. It characterized by the collaborative search in which each particle attracted toward global best position (gbest) and its own (pbest). However, all of particles' historical promising pbests PSO are lost except their current pbests. In order to solve this problem, paper proposes novel composite algorithm, called memory-based (HMPSO), uses an estimation distribution estimate preserve information Each has...

Particle swarm optimizer (PSO) is a population-based optimization technique applied to wide range of problems. In the literature, many PSO variants have been proposed deal with noise-free or noisy environments, respectively. While in real-life applications, noise emerges irregularly and unpredictably. As result, for environment loses its accuracy when exists, while wastes resampling resource does not exist. To handle such scenario, variant that can work well both environments required, which...

An update scheme of the state probability vector actions is critical for learning automata (LA). The most popular pursuit that pursues estimated optimal action and penalizes others. This paper proposes a reverse philosophy leads to last-position elimination-based (LELA). graded last in terms performance penalized by decreasing its eliminated when becomes zero. All active actions, is, with nonzero probability, equally share from at each iteration. proposed LELA characterized relaxed...

With the recent proliferation of use text classifications, researchers have found that there are certain unintended biases in classification datasets. For example, texts containing some demographic identity-terms (e.g., “gay”, “black”) more likely to be abusive existing language detection As a result, models trained with these datasets may consider sentences like “She makes me happy gay” as simply because word “gay.” In this paper, we formalize kind selection bias from non-discrimination...

Locating multiple sources in an unknown environment based on their signal strength is called a multi-source location problem. In recent years, there has been great interest deploying autonomous devices to solve it. A particle swarm optimizer (PSO) widely employed source method. Yet most work this field focuses flat search space while ignoring height information. An unmanned aerial vehicle (UAV) coarser but wider view as it flies higher. Inspired by such facts, paper improving the efficiency...

A vehicle's license plate is the unique feature by which to identify each individual vehicle. As an important research area of intelligent transportation system, recognition vehicle plates has been investigated for some decades. An approach based on a visual attention model and deep learning proposed handle problem Chinese car traffic videos. We first use modified locate plate, then segmented into seven blocks using projection method. Two classifiers, combine advantages convolutional neural...

A learning automaton (LA) is a powerful tool for reinforcement learning. Its action probability vector plays two roles: 1) deciding when it converges, i.e., total computing budget has used, and 2) allocating among actions to identify the optimal one. These intertwined roles lead problem: mostly goes currently estimated due its high regardless whether such allocation can help true one or not. This work proposes new class of LA that avoids use allocation. Instead we only determine if converges...

Particle swarm optimizer (PSO) and mobile robot are two typical techniques. Many applications emerge separately along both of them while the similarity between is rarely considered. When a solution space certain region in reality, can replace particle to explore optimal by performing PSO. In this way, should be able efficiently an area just like uninterruptedly work even under shortage robots or case unexpected failure robots. Furthermore, moving distances highly constrained because energy...

Introduction: Small-scaled robotic walkers play an increasingly important role in Activity of Daily Living (ADL) assistance the face ever-increasing rehab requirements and existing equipment drawbacks. This paper proposes a Rehabilitation Robotic Walker (RRW) for walking body weight support (BWS) during gait rehabilitation. Methods: The walker provides patients with offloading guiding force to mimic series physiotherapist’s (PT’s) movements, creates natural, comfortable, safe environment....

Learning automata (LA) are powerful tools for reinforcement learning. A discretized pursuit LA is the most popular one among them. During an iteration its operation consists of three basic phases: 1) selecting next action; 2) finding optimal estimated and 3) updating state probability. However, when number actions large, learning becomes extremely slow because there too many updates to be made at each iteration. The increased mostly from phases 1 3. new fast with assured ε -optimality...

Research on image sensor (IS)-based visible light positioning systems has attracted widespread attention. However, when the receiver is tilted or under a single LED, system can only achieve two-dimensional (2D) and requires assistance of inertial measurement units (IMU). When LED not captured decoding fails, system's error increases further. Thus, we propose novel three-dimensional (3D) based sensors for various environments. Specifically, 1) We use IMU to obtain receiver's state calculate...

Research on image sensor (IS)-based visible light positioning systems has attracted widespread attention. However, when the receiver is tilted or under a single LED, system can only achieve two-dimensional (2D) and requires assistance of inertial measurement units (IMU). When LED not captured decoding fails, system's error increases further. Thus, we propose novel three-dimensional (3D) based sensors for various environments. Specifically, 1) We use IMU to obtain receiver's state calculate...

Road network persistent surveillance requires a swarm of unmanned ground vehicles (UGVs) to repeatedly surveil sequence places called viewpoints on road and detect randomly occurring events problems at viewpoints. In most existing work, UGVs are responsible for detection but not handling emergencies like extinguishing fires, capturing intruders, or managing pollution. Hence, methods fail perform both emergency handling. This work copes with proposes cooperative dual-task path planning method...

In this paper, we present a new approach to detect traffic signs based on cascaded convolutional neural networks (CNNs). First, the local binary pattern (LBP) feature detector and AdaBoost classifier are combined extract regions of interest (ROI) for coarse selection. Next, CNNs employed reduce negative samples ROI sign recognition. Compared with conventional CNN, our CNN contains three layers its classification part is replaced by support vector machine (SVM). The German detection benchmark...

This paper introduces a novel particle swarm optimization algorithm based on the concept of black holes in physics, called random hole (RBH-PSO) for first time. In each dimension particle, we randomly generate located nearest to best current generation and then pull particles into with probability p. By this mechanism hole, can give all another interesting direction converge as well chance fly out local minima when premature convergence happens. Several experiments fifteen benchmark test...

Particle swarm optimizer (PSO) is an optimization technique that has been applied to solve various problems. In its variants, hierarchical learning and variable population are two commonly used strategies. The former employ more potentially good particles lead the swarm, which very effective in early search phase. However, later phase, such mechanism impedes PSO's convergence. This work proposes adaptive particle combining with (PSO-HV), a heap-based hierarchy first proposed organize...

Accurate performance evaluation of discrete event systems needs a huge number simulation replications and is thus time-consuming costly. Hence, efficiency always big concern when simulations are conducted. To drastically reduce its cost conducting them, ordinal optimization emerges. further enhance the optimization, optimal computing budget allocation (OCBA) proposed to decide best design accurately quickly. Its variants have been introduced achieve goals with distinct assumptions, such as...

A stochastic point location (SPL) problem aims to find a target parameter on 1-D line by operating controlled random walk and receiving information from environment (SE). If the changes randomly, we call dynamic; otherwise static. SE can be 1) informative (p > 0.5 where p represents probability for an providing correct suggestion) 2) deceptive <; 0.5). Up till now, hierarchical searching (HSSL) is most efficient algorithms catch static or dynamic in environment, but unable locate recognize...

Particle Swarm Optimization (PSO) is an outstanding evolutionary algorithm designed to tackle various optimization problems. However, its performance deteriorates significantly in noisy environments. Some studies have addressed this issue by introducing a resampling method. Most existing methods allocate fixed and predetermined budget of re-evaluations for every iteration, but cannot change the according different environments adaptively. Our previous work proposed PSO-LA integrate PSO with...

An attack–defense confrontation problem arises from a robot swarm attacking territory protected by another one. In denied environments, global positioning and communication are hardly available. It becomes difficult for to realize collaboration handle against another. Commonly used deep reinforcement learning (DRL) relies on pretraining, which is time consuming has strong environmental dependence, especially in environments. To study attack strategies this work proposes novel evolutionary...

Ordinal optimization is an efficient technique to choose and rank various engineering designs that require time-consuming discrete-event simulations. Optimal computing budget allocation (OCBA) has been important tool enhance its efficiency such the best design selected in a timely fashion. It, however, fails address issue of selecting worst efficiently. The need select both rapidly given fixed arisen from many applications. This work develops new OCBA-based approach for at same time. Its...

Coverage path planning (CPP) is essential for robotic tasks, such as environmental monitoring and terrain surveying, which require covering all surface areas of interest. As the pioneering approach to CPP, inspired by concept predation risk in predator–prey relations, CPP (PPCPP) has benefit adaptively arbitrary bent 2-D manifolds can handle unexpected changes an environment, sudden introduction dynamic obstacles. However, it only work bounded environment cannot tasks unbounded one, e.g.,...