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

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

A survey of swarm intelligence for dynamic optimization: algorithms and applications

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method

Enhanced Bees Algorithm with fuzzy logic and Kalman filtering

The Bees Algorithm is a new population-based optimisation procedure which employs a combination of global exploratory and local exploitatory search. This thesis introduces an enhanced version of the Bees Algorithm which implements a fuzzy logic system for greedy selection of local search sites. The proposed fuzzy greedy selection system reduces the number of parameters needed to run the Bees Algorithm. The proposed algorithm has been applied to a number of benchmark function optimisation problems to demonstrate its robustness and self-organising ability. The Bees Algorithm in both its basic and enhanced forms has been used to optimise the parameters of a fuzzy logic controller. The purpose of the controller is to stabilise and balance an under-actuated two-link acrobatic robot (ACROBOT) in the upright position. Kalman filtering, as a fast convergence gradient-based optimisation method, is introduced as an alternative to random neighbourhood search to guide worker bees speedily towards the optima of local search sites. The proposed method has been used to tune membership functions for a fuzzy logic system. Finally, the fuzzy greedy selection system is enhanced by using multiple independent criteria to select local search sites. The enhanced fuzzy selection system has again been used with Kalman filtering to speed up the Bees Algorithm. The resulting algorithm has been applied to train a Radial Basis Function (RBF) neural network for wood defect identification. The results obtained show that the changes made to the Bees Algorithm in this research have significantly improved its performance. This is because these enhancements maintain the robust global search attribute of the Bees Algorithm and improve its local search procedure.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Heuristic design of fuzzy inference systems: a review of three decades of research

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multi-objective fuzzy systems (MFS), which is in view that some of them are linked to each other. The heuristic design of GFS uses evolutionary algorithms for optimizing both Mamdani-type and Takagi–Sugeno–Kang-type fuzzy systems. Whereas, the NFS combines the FIS with neural network learning systems to improve the approximation ability. An HFS combines two or more low-dimensional fuzzy logic units in a hierarchical design to overcome the curse of dimensionality. An EFS solves the data streaming issues by evolving the system incrementally, and an MFS solves the multi-objective trade-offs like the simultaneous maximization of both interpretability and accuracy. This paper offers a synthesis of these dimensions and explores their potentials, challenges, and opportunities in FIS research. This review also examines the complex relations among these dimensions and the possibilities of combining one or more computational frameworks adding another dimension: deep fuzzy systems

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Enhanced Bees Algorithm with fuzzy logic and Kalman filtering

The Bees Algorithm is a new population-based optimisation procedure which employs a combination of global exploratory and local exploitatory search. This thesis introduces an enhanced version of the Bees Algorithm which implements a fuzzy logic system for greedy selection of local search sites. The proposed fuzzy greedy selection system reduces the number of parameters needed to run the Bees Algorithm. The proposed algorithm has been applied to a number of benchmark function optimisation problems to demonstrate its robustness and self-organising ability. The Bees Algorithm in both its basic and enhanced forms has been used to optimise the parameters of a fuzzy logic controller. The purpose of the controller is to stabilise and balance an under-actuated two-link acrobatic robot (ACROBOT) in the upright position. Kalman filtering, as a fast convergence gradient-based optimisation method, is introduced as an alternative to random neighbourhood search to guide worker bees speedily towards the optima of local search sites. The proposed method has been used to tune membership functions for a fuzzy logic system. Finally, the fuzzy greedy selection system is enhanced by using multiple independent criteria to select local search sites. The enhanced fuzzy selection system has again been used with Kalman filtering to speed up the Bees Algorithm. The resulting algorithm has been applied to train a Radial Basis Function (RBF) neural network for wood defect identification. The results obtained show that the changes made to the Bees Algorithm in this research have significantly improved its performance. This is because these enhancements maintain the robust global search attribute of the Bees Algorithm and improve its local search procedure

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field