Abstract Throughout the centuries, nature has been a source of inspiration, with much still to learn from and discover about. Among many others, Swarm Intelligence (SI), a substantial branch of Artificial Intelligence, is built on the intelligent collective behavior of social swarms in nature. One of the most popular SI paradigms, the Particle Swarm Optimization algorithm (PSO), is presented in this work. Many changes have been made to PSO since its inception in the mid 1990s. Since their learning about the technique, researchers and practitioners have developed new applications, derived new versions, and published theoretical studies on the potential influence of various parameters and aspects of the algorithm. Various perspectives are surveyed in this paper on existing and ongoing research, including algorithm methods, diverse application domains, open issues, and future perspectives, based on the Systematic Review (SR) process. More specifically, this paper analyzes the existing research on methods and applications published between 2017 and 2019 in a technical taxonomy of the picked content, including hybridization, improvement, and variants of PSO, as well as real-world applications of the algorithm categorized into: health-care, environmental, industrial, commercial, smart city, and general aspects applications. Some technical characteristics, including accuracy, evaluation environments, and proposed case study are involved to investigate the effectiveness of different PSO methods and applications. Each addressed study has some valuable advantages and unavoidable drawbacks which are discussed and has accordingly yielded some hints presented for addressing the weaknesses of those studies and highlighting the open issues and future research perspectives on the algorithm. 

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Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review

Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided. 

Particle Swarm Optimization: A Comprehensive Survey

Particle swarm optimization (PSO) is a population based meta-heuristic search algorithm that has been widely applied to a variety of problems since its advent. In PSO, the inertial weight not only has a crucial effect on its convergence, but also plays an important role in balancing exploration and exploitation during the evolution. However, PSO is easily trapped into the local optima and premature convergence appears when applied to complex multimodal problems. To address these issues, we present a modified particle swarm optimization with chaos-based initialization and robust update mechanisms. On the one side, the Logistic map is utilized to generate uniformly distributed particles to improve the quality of the initial population. On the other side, the sigmoid-like inertia weight is formulated to make the PSO adaptively adopt the inertia weight between linearly decreasing and nonlinearly decreasing strategies in order to achieve better tradeoff between the exploration and exploitation. During this process, a maximal focus distance is formulated to measure the particle's aggregation degree. At the same time, the wavelet mutation is applied for the particles whose fitness value is less than that of the average so as to enhance the swarm diversity. In addition, an auxiliary velocity-position update mechanism is exclusively applied to the global best particle that can effectively guarantee the convergence of MPSO. Extensive experiments on CEC′13/15 test suites and in the task of standard image segmentation validate the effectiveness and efficiency of the MPSO algorithm proposed in this paper.

MPSO: Modified particle swarm optimization and its applications

The “curse of dimensionality” is one of the largest problems that influences the quality of the optimization process in most data mining, pattern recognition, and machine learning tasks. Using high-dimensional datasets to train a classification model may reduce the generalization performance of the learned model. In addition, high dimensionality of the dataset results in high computational and memory costs. Feature selection is an important data preprocessing approach in many practical application domains that are relevant to expert and intelligent systems. Feature selection aims at selecting a subset of informative and relevant features from an original feature dataset. Therefore, using a feature selection approach to process the original data prior to the learning process is essential for enhancing the performance on the classification task. In this paper, hybrid particle swarm optimization with a spiral-shaped mechanism (HPSO-SSM) is proposed for selecting the optimal feature subset for classification via a wrapper-based approach. In HPSO-SSM, we make three improvements: First, a logistic map sequence is used to enhance the diversity in the search process. Second, two new parameters are introduced into the original position update formula, which can effectively improve the position quality of the next generation. Finally, a spiral-shaped mechanism is adopted as a local search operator around the known optimal solution region. For a complete evaluation, the proposed HPSO-SSM method is compared with six state-of-the-art meta-heuristic optimization algorithms, ten well-known wrapper-based feature selection techniques, and six classic filter-based feature selection methods. Various assessment indicators are used to properly evaluate and compare the performances of these approaches on twenty classic benchmark classification datasets from the UCI machine learning repository. According to the experimental results and statistical tests, the developed methods effectively and efficiently improve the classification accuracy compared with other wrapper-based approaches and filter-based approaches. The results demonstrate the high performance of the HPSO-SSM method in searching the feasible feature space and selecting the most informative attributes for solving classification problems. Therefore, the HPSO-SSM method has broad application prospects as a new feature selection approach.

Hybrid particle swarm optimization with spiral-shaped mechanism for feature selection

Genetic learning particle swarm optimization (GL-PSO) improves the performance of particle swarm optimization (PSO) by breeding superior exemplars to guide the motion of particles. However, GL-PSO adopts a global topology for exemplar generation and cannot preserve sufficient diversity to enhance exploration, and therefore, its performance on complex optimization problems is unsatisfactory. To further improve GL-PSO's performance and adaptability, two modifications are incorporated into the original GL-PSO. A ring topology is adopted in exemplar generation to enhance diversity and exploration, while a global learning component (GLC) with linearly adjusted control parameters is employed to improve the algorithm's adaptability. The resultant algorithm is referred to as global genetic learning particle swarm optimization with diversity enhancement by ring topology (GGL-PSOD). To validate the effectiveness of these two modifications, they are combined with GL-PSO separately and together and further tested experimentally. The comparison results on the CEC2017 test suite show that the adoption of the ring topology in exemplar generation can enhance the diversity and exploration capability of GL-PSO, while combining GLC alone with GL-PSO cannot achieve significant improvement. Incorporating both modifications into GL-PSO, the resultant GGL-PSOD exhibits high performance and strong adaptability on different types of CEC2017 functions. It outperforms seven representative PSO variants and five non-PSO meta-heuristics, including GL-PSO, SL-PSO, HCLPSO, EPSO, ABC, CMA-ES and CS.

Global genetic learning particle swarm optimization with diversity enhancement by ring topology

A hybrid particle swarm optimizer with sine cosine acceleration coefficients

The invention discloses a robot system based on intelligent sound localization and voice control, and a method A robot body collects ambient voice information continuously, when a voice command occurs, sound localization is conducted, the robot body is controlled to move to the sound source position, the collected voice information is recognized, when effective sentences are recognized, a corresponding control command is sent to the robot body, and the robot body executes corresponding operation; and meanwhile, the effective sentences are converted into corresponding characters so that Chinese word segmentation can be carried out, an affective dictionary, a degree adverb dictionary, a negative word vocabulary and an associated word vocabulary are loaded, all affective characters in the sentences are identified, and a corresponding facial expression is displayed according to the identification result Through the robot system based on intelligent sound localization and voice control, and the method, the interactive ability of a service robot with an accompanied person can be improved

Robot system based on intelligent sound localization and voice control and method

The invention discloses an autonomous charging system of a household accompanying robot based on a ROS and a method thereof. The autonomous charging system settles problems of insufficient butt-jointtolerance of a household accompanying robot charging station structure, shortage of common environment adaptability, relatively low charging efficiency, long butt-joint process, etc. The autonomous charging system has autonomous charging capability. When a robot is in a low-electric-quantity state in a working process, the position of the charging station can be autonomously searched, and accuratebutt-jointing with the charging station is performed. The autonomous charging system is characterized in that mileometer data are obtained from an encoder and IMU data through Kalman filtering; an environment two-dimensional grid map is constructed according to a mileometer and laser radar data based on a Rao-Blackwellized particle filtering SLAM algorithm, positioning is performed by means of the generated map; then remote abut-jointing is realized through path programming based on an A* and DWA algorithm, so that a robot moves to periphery of the charging station. Finally short-distance butt-jointing is realized through an infrared and ultrasonic sensors based on dual priorities, thereby finishing autonomous charging.

Autonomous charging system of household accompanying robot and method thereof

The invention discloses a data feature selection method and a system based on an improved particle swarm algorithm, comprising the following steps: a classifier model of an evaluation feature subset is determined; the correct classification rate of the classifier model is used to guide feature selection, and the number of feature subsets is gradually added to guide feature selection, and the quality evaluation function of feature subsets is established; criteria for feature selection in a dataset are determined; the particle swarm optimization (PSO) algorithm is improved. According to the quality evaluation function of the feature subset, the fitness of each particle in the PSO is calculated, and the inertia weight of the PSO algorithm is updated by Logistic chaotic atlas, so that the improved PSO algorithm can process the data set and obtain the selection results.

Data feature selection method and system based on improved particle swarm algorithm

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A hybrid particle swarm optimizer with sine cosine acceleration coefficients

Robot system based on intelligent sound localization and voice control and method

Autonomous charging system of household accompanying robot and method thereof

Data feature selection method and system based on improved particle swarm algorithm