Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

The fifth edition of "Numerical Methods for Engineers" continues its tradition of excellence. Instructors love this text because it is a comprehensive text that is easy to teach from. Students love it because it is written for them--with great pedagogy and clear explanations and examples throughout. The text features a broad array of applications, including all engineering disciplines. The revision retains the successful pedagogy of the prior editions. Chapra and Canale's unique approach opens each part of the text with sections called Motivation, Mathematical Background, and Orientation, preparing the student for what is to come in a motivating and engaging manner. Each part closes with an Epilogue containing sections called Trade-Offs, Important Relationships and Formulas, and Advanced Methods and Additional References. Much more than a summary, the Epilogue deepens understanding of what has been learned and provides a peek into more advanced methods. Approximately 80% of the end-of-chapter problems are revised or new to this edition. The expanded breadth of engineering disciplines covered is especially evident in the problems, which now cover such areas as biotechnology and biomedical engineering. Users will find use of software packages, specifically MATLAB and Excel with VBA. This includes material on developing MATLAB m-files and VBA macros.

Numerical methods for engineers

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches.

In this paper, we investigate the trajectory tracking problem for a fully actuated autonomous underwater vehicle (AUV) that moves in the horizontal plane. External disturbances, control input nonlinearities and model uncertainties are considered in our control design. Based on the dynamics model derived in the discrete-time domain, two neural networks (NNs), including a critic and an action NN, are integrated into our adaptive control design. The critic NN is introduced to evaluate the long-time performance of the designed control in the current time step, and the action NN is used to compensate for the unknown dynamics. To eliminate the AUV’s control input nonlinearities, a compensation item is also designed in the adaptive control. Rigorous theoretical analysis is performed to prove the stability and performance of the proposed control law. Moreover, the robustness and effectiveness of the proposed control method are tested and validated through extensive numerical simulation results.

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Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning

Fuzzy logic control, due to its simple control structure, easy and cost-effective design, has been successfully employed to the application of guidance and control in robotic fields. This paper aims to review fuzzy-logic-based guidance and control in an important branch of robots—marine robotic vehicles. First, guidance and motion forms including the maneuvering, path following, trajectory tracking, and position stabilization are described. Subsequently, the application of three major classes of fuzzy logic control, including the conventional fuzzy control (Mamdani fuzzy control and Takagi–Sugeno–Kang fuzzy control), adaptive fuzzy control (self-tuning fuzzy control and direct/indirect adaptive fuzzy control), and hybrid fuzzy control (fuzzy PID control, fuzzy sliding mode control, and neuro-fuzzy control) are presented. In particular, we summarize the design and analysis process of direct/indirect adaptive fuzzy control and fuzzy PID control in marine robotic fields. In addition, two comparative results between hybrid fuzzy control and the corresponding single control are provided to illustrate the superiority of hybrid fuzzy control. Finally, trends of the fuzzy future in marine robotic vehicles are concluded based on its state of the art.

Survey on Fuzzy-Logic-Based Guidance and Control of Marine Surface Vehicles and Underwater Vehicles

This paper focuses on design of a new self-adaptive fuzzy PID controller based on nonlinear MIMO structure for an AUV. Complexity and highly coupled dynamics, time-variance, and difficulty in hydrodynamic modeling and simulation, complicates the AUV modeling process and the design of proper and acceptable controller. In this work, the comprehensive nonlinear model of AUV is derived through kinematics and dynamic equations and then its treatment in open-loop is verified. In proposed controller, the PID parameters are adjusted by Mamdani fuzzy rules. Combined adaptive methods and dual PID controllers can improve solving of the uncertainty challenge in the PID parameters and AUV modeling uncertainty. The simulation results indicate that developed control system is stable, competent, and efficient enough to control the AUV in tracking the two channels of heading and depth with stabilized speed. Obtained results show that the proposed controller is not only robust, but also gives excellent dynamic, stunning steady-state characteristics and robust stability compared with a classically tuned PID controller.

Modeling and control of autonomous underwater vehicle (AUV) in heading and depth attitude via self-adaptive fuzzy PID controller

In practical applications, the pure derivative action is never used, due to the “derivative kick” produced in the control signal for a step input, and to the undesirable noise amplification. It is usually replaced by a first-order low-pass filter. In this paper, we use a $$\mu $$
 -order fractional low-pass filter and define a practical fractional-order controller. The proposed approach with new defined fitness function has very easy implementation and the most control performance. We present a method for optimum tuning of practical fractional PID controllers for automatic voltage regulator system using particle swarm optimization (PSO) algorithm. PSO is a robust stochastic optimization technique based on the movement and intelligence of swarm, applies the concept of social interaction to problem solving. From the comparison this technique with the other methods, its influence and efficiency are illustrated. Simulations and comparisons with other FOPID/PID controllers illustrate that the proposed PSO-FOPID controller can provide good control performance with respect to reference input and also improve the system robustness with respect to model uncertainties.

Design of Optimal Fractional-Order PID Controllers Using Particle Swarm Optimization Algorithm for Automatic Voltage Regulator (AVR) System

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of “intelligent” global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.

Research in SI started in the late 1980s. Besides the applications to conventional optimization problems, SI can be employed in library materials acquisition, communications, medical dataset classification, dynamic control, heating system planning, moving objects tracking, and prediction. Indeed, SI can be applied to a variety of fields in fundamental research, engineering, industries, and social sciences.

The main objective of this special issue is to provide the readers with a collection of high quality research articles that address the broad challenges in application aspects of swarm intelligence and reflect the emerging trends in state-of-the-art algorithms. 

The special issue received 42 high quality submissions from different countries all over the world. All submitted papers followed the same standard (peer-reviewed by at least three independent reviewers) as applied to regular submissions to “this journal”. Due to the limited space, 15 papers were finally included. The primary guideline was to demonstrate the wide scope of SI algorithms and applications in various aspects. Besides, mathematically oriented papers with promising potential in practical problems were also included.

The paper authored by Y.-L. Wu et al. (National Chiao Tung University and Ming Chuan University) presents an integer programming model of the studied problem by considering how to select materials in order to maximize the average preference and the budget execution rate under some practical restrictions including departmental budget and limitation of the number of materials in each category and each language. They propose a discrete particle swarm optimization (DPSO) with scout particles, design an initialization algorithm and a penalty function to cope with the constraints, and employ the scout particles to enhance the exploration within the solution space. 

In the paper by Z. Yin et al. (Harbin Institute of Technology), they propose an efficient multiuser detector based on a suboptimal code mapping multiuser detector and artificial bee colony algorithm (SCM-ABC-MUD) and implement the proposed algorithm in direct-sequence ultrawideband (DS-UWB) systems under the additive white Gaussian noise (AWGN) channel.

M. S. Uzer et al. (Selcuk University) offer a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. For the diagnosis of hepatitis, liver disorders, and diabetes datasets from the UCI database, the proposed system reached classification accuracies of 94.92%, 74.81%, and 79.29%, respectively.

Another paper is by M. Karakose (Firat University) and U. Cigdem (Gaziosmanpasa University). It proposes a new approach for improvement of DNA computing with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Experimental results obtained with MATLAB and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm.

In the paper by Y. Celik (Karamanoglu Mehmetbey University) and E. Ulker (Selcuk University), their research proposes an improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.

M. Baygin (Ardahan University) and M. Karakose (Firat University) study a new approach of immune system-based optimal estimate for dynamic control of group elevator systems. The method is mainly based on estimation of optimal way by optimizing all calls with genetic, immune system and DNA computing algorithms, and it is evaluated with a fuzzy system. With dynamic and adaptive control approach in this study, a significant progress on group elevator control systems has been achieved in terms of time and energy efficiency according to traditional methods.

The paper by M. Karakose (Firat University) proposes a reinforcement-learning based artificial immune classifier. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method.

In their paper, T. J. Choi et al. (Sungkyunkwan University) and (Daegu Gyeongbuk Institute of Science and Technology) present an adaptive parameter control DE algorithm. The control parameters of each individual are adapted based on the average of successfully evolved individuals' parameter values using the Cauchy distribution. The experimental results show that their proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems.

In the paper by R.-J. Ma et al. (Southwest Jiaotong University and CSR Qishuyan Institute Co., Ltd.), the authors present an integral mathematical model and particle swarm optimization (PSO) algorithm based on the life cycle cost (LCC) approach for the heating system planning (HSP) problem. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm.

In the paper by M. Tang et al. (National University of Defense Technology and Universite Pierre et Marie Curie), they report that the flocking has some negative effects on the human, as the infectious disease H7N9 will easily be transmitted from the denser flocking birds to the human. Their paper focuses on the H7N9 virus transmission in the flocking birds and from the flocking birds to the human. Some interesting results have been shown: (1) only some simple rules could result in an emergence such as the flocking; (2) the minimum distance between birds could affect H7N9 virus transmission in the flocking birds and even affect the virus transmissions from the flocking birds to the human.

Y. Wang et al. (China University of Petroleum) present a memory-based multiagent coevolution algorithm for robust tracking the moving objects. Each agent can remember, retrieve, or forget the appearance of the object through its own memory system by its own experience. Experimental results show that their proposed method can deal with large appearance changes and heavy occlusions when tracking a moving object. 

The paper by Q. Ni and J. Deng (Southeast University and Soochow University) analyzes the performance of PSO with the proposed random topologies and explores the relationship between population topology and the performance of PSO from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. 

Y. Zhou and H. Zheng (Guangxi University for Nationalities, Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis) propose a novel complex valued cuckoo search algorithm. They use complex-valued encoding to expand the information of nest individuals and denote the gene of individuals by plurality. The value of independent variables for objective function is determined by modules, and a sign of them is determined by angles. The position of nest is divided into real part gene and imaginary gene. Six typical functions are tested, and the usefulness of the proposed algorithm is verified.

The paper by R. Alwee et al. (Universiti Teknologi Malaysia) introduces a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. Particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The experimental results show that their proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

Finally, K. S. Lim et al. (Universiti Teknologi Malaysia, Universiti Malaysia Pahang, and University of Malaya) describe an improved Vector Evaluated Particle Swarm Optimization algorithm by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. The results suggest that the improved Vector Evaluated Particle Swarm Optimization algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimization algorithm.

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Swarm intelligence and its applications.

1 School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China 2Department of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Khorasan-e-Razavi 96916-29, Iran 3Department of Mathematics, Anand International College of Engineering, Agra Road, Near Bassi, Jaipur, Rajasthan 303012, India 4Ambedkar Institute of Advanced Communication Technologies and Research, Government of NCT of Delhi, Geeta Colony, Delhi 110031, India 5 Department of Chemistry, Arab American University, 19356 Jenin, Palestine

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Artificial Intelligence and Its Applications

Increase in complexity of real world problems has provided an area to explore efficient methods to solve computer science problems. Meta-heuristic methods based on evolutionary computations and swarm intelligence are instances of techniques inspired by nature. This paper presents a novel social mimic optimization (SMO) algorithm inspired by mimicking behavior to solve optimization problems. The proposed algorithm is evaluated using 23 test functions. Obtained results are compared with 14 known optimization algorithms including Whale optimization algorithm (WOA), Grasshopper optimization algorithm (GOA), Particle Swarm Optimization (PSO), Stochastic fractal search (SFS), Grey Wolf Optimizer (GWO), Optics Inspired Optimization (OIO), League Championship Algorithm (LCA), Wind Driven Optimization (WDO), Harmony search (HS), Firefly Algorithm (FA), Artificial Bee Colony (ABC), Biogeography Based Optimization (BBO), Bat Algorithm (BA), and Teaching Learning Based Optimization (TLBO). Obtained results indicate higher capability of the SMO algorithm in solving high-dimensional decision variables. Furthermore, SMO is used to solve two classic engineering design problems. Three important features of SMO are simple implementation, solving optimization problems with minimum population size and not requiring control parameters. Results of various evaluations show superiority of the proposed method in finding the optimal solution with minimum function evaluations. This superiority is achieved based on reducing number of initial population. The proposed method can be applied to applications like automatic evolution of robotics, automatic control of machines and innovation of machines in finding better solutions with less cost.

Saeed Balochian

Papers

Modeling and control of autonomous underwater vehicle (AUV) in heading and depth attitude via self-adaptive fuzzy PID controller

Design of Optimal Fractional-Order PID Controllers Using Particle Swarm Optimization Algorithm for Automatic Voltage Regulator (AVR) System

Swarm intelligence and its applications.

Artificial Intelligence and Its Applications

Social mimic optimization algorithm and engineering applications