Swarm Intelligence

Swarm Intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting the fastest growing stream in the bio-inspired computation community. A clear trend can be deduced analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has increased at a notable pace in the last years. This book describes the prominent theories and recent developments of Swarm Intelligence methods, and their application in all fields covered by engineering. This book unleashes a great opportunity for researchers, lecturers, and practitioners interested in Swarm Intelligence, optimization problems, and artificial intelligence

Path Optimization and Object Localization Using Hybrid Particle Swarm and Ant Colony Optimization for Mobile RFID Reader

This paper proposes a hybrid approach of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for the mobile Radio Frequency Identification System (RFID) reader to get the shortest path for object localization. In this approach, we have adopted the ACO global pheromone updating information of ants to guide the update velocities and position for PSO based on nearest neighbor constraints. The pheromone information is used efficiently to guide the selection of each particle in a search space of its visits. The best path will be used for mobile RFID reader for objects localization in search space. Simulation results show that the method is effective, minimizing the number of visited nodes for a mobile RFID reader

A Hankel Matrix Based Reduced Order Model for Stability Analysis of Hybrid Power System Using PSO-GSA Optimized Cascade PI-PD Controller for Automatic Load Frequency Control

This paper presents the automatic load frequency control (ALFC) of two-area multisource hybrid power system (HPS). The interconnected HPS model consists of conventional and renewable energy sources operating in disparate combinations to balance the generation and load demand of the system. In the proffered work, the stability analysis of nonlinear dynamic HPS model was analyzed using the Hankel method of model order reduction. Also, an attempt was made to apply cascade proportional integral - proportional derivative (PI-PD) control for HPS. The gains of the controller were optimized by minimizing the integral absolute error (IAE) of area control error using particle swarm optimization-gravitational search algorithm (PSO-GSA) optimization technique. The performance of cascade control was compared with other classical controllers and the efficiency of this approach was studied for various cases of HPS model. The result shows that the cascade control produced better transient and steady state performances than those of the other classical controllers. The robustness analysis also reveals that the system overshoots/undershoots in frequency response pertaining to random change in wind power generation and load perturbations were significantly reduced by the proposed cascade control. In addition, the sensitivity analysis of the system was performed, with the variation in step load perturbation (SLP) of 1% to 5%, system loading and inertia of the system by ±25% of nominal values to prove the efficiency of the controller. Furthermore, to prove the efficiency of PSO-GSA tuned cascade control, the results were compared with other artificial intelligence (AI) methods presented in the literature. Further, the stability of the system was analyzed in frequency domain for different operating cases

Artificial Intelligent Tuning PI Controller on Wind Turbine System with Three-Phase Grid Connected System

In order to generate the electricity from the wind turbine system with a permanent magnet synchronous generator (PMSG) to the three-phase grid connected system, it is very challenging because the electricity generated from wind energy resource is inconstancy. In this paper, the electrical power between the two systems are regulated by PI controller. For tuning the gains of PI controller, artificial intelligent methods, particle swarm optimization (PSO), gravitational search algorithm (GSA), artificial neural networks (ANN), are applied. The experiments of the control systems are testing at 200 w and 400 w. The experimental results show that the rise time of an electrical power of control system at 200 w tuning gains of the PI controller by ANN combined with PSO is better than the traditional tuning methods, pole placement. Therefore, the proposed intelligent system can not only reduce the overshoot of the active power but also improve the response power

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

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

Investigation of performance of fuzzy logic controllers optimized with the hybrid genetic-gravitational search algorithm for PMSM speed control

Fuzzy logic controllers (FLCs) are widely used to control complex systems with model uncertainty, such as alternating current motors. The design process of the FLC is generally based on the designer’s adjustments on the controller until the desired performance is achieved. However, doing the controller design in this way makes the design process quite difficult and time-consuming, so it is often impossible to make a suitable and successful design. In this study, the output membership functions of the FLC are optimized with heuristic algorithms to reach the best speed control performance of the permanent magnet synchronous motor (PMSM). This paper proposes a new hybrid algorithm called H-GA-GSA, created by combining the advantages of the Genetic Algorithm (GA) and Gravitational Search Algorithm (GSA) to optimize FLC. The paper presents a convenient adjustment and design method for optimizing FLC with heuristic algorithms considered. To evaluate the effectiveness of H-GA-GSA, the proposed hybrid algorithm has been compared with GA and GSA in terms of convergence rate, PMSM speed control performance and electromagnetic torque variations. Optimization performance and results obtained from simulation studies verify that the proposed hybrid H-GA-GSA outperforms GA and GSA

A review of recent advances in metaheuristic maximum power point tracking algorithms for solar photovoltaic systems under the partial-shading conditions

Several maximum power point (MPP) tracking algorithms for solar power or photovoltaic (PV) systems concerning partial-shading conditions have been studied and reviewed using conventional or advanced methods. The standard MPPT algorithms for partial-shading conditions are: (i) conventional; (ii) mathematics-based; (iii) artificial intelligence; (iv) metaheuristic. The main problems of the conventional methods are poor power harvesting and low efficiency due to many local maximum appearances and difficulty in determining the global maximum tracking. This paper presents MPPT algorithms for partial-shading conditions, mainly metaheuristics algorithms. Firstly, the four classification algorithms will be reviewed. Secondly, an in-depth review of the metaheuristic algorithms is presented. Remarkably, 40 metaheuristic algorithms are classified into four classes for a more detailed discussion; physics-based, biology-based, sociology-based, and human behavior-based are presented and evaluated comprehensively. Furthermore, the performance comparison of the 40 metaheuristic algorithms in terms of complexity level, converter type, sensor requirement, steady-state oscillation, tracking capability, cost, and grid connection are synthesized. Generally, readers can choose the most appropriate algorithms according to application necessities and system conditions. This study can be considered a valuable reference for in-depth works on current related issues