Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.

Grey wolf optimizer: a review of recent variants and applications

Flower pollination algorithm (FPA) is a computational intelligence metaheuristic that takes its metaphor from flowers proliferation role in plants. This paper provides a comprehensive review of all issues related to FPA: biological inspiration, fundamentals, previous studies and comparisons, implementation, variants, hybrids, and applications. Besides, it makes a comparison between FPA and six different metaheuristics such as genetic algorithm, cuckoo search, grasshopper optimization algorithm, and others on solving a constrained engineering optimization problem
. The experimental results are statistically analyzed with non-parametric Friedman test which indicates that FPA is superior more than other competitors in solving the given problem.

Flower pollination algorithm: a comprehensive review

This paper introduces a comprehensive overview of the Ant Lion Optimizer (ALO). ALO is a novel metaheuristic swarm-based approach introduced by Mirjalili in 2015 to emulate the hunting behavior of ant lions in nature life. The review is highlighted the applications that are utilized ALO algorithm to solve various optimization problems. In ALO, the best solution is determined to enhance the performance of the functional and efficient during the optimization process by finding the minimum or maximum values to solve a certain problem. Metaheuristic algorithms have become the focus of research due to introduce of decision-making and asses the benefits in solving various optimization problems. Also, a review of ALO variants is presented in this paper such as binary, modification, hybridization, enhanced, and others. The classifications of the ALO’s applications include the benchmark functions, machine learning applications, networks applications, engineering applications, software engineering, and Image processing. Finally, According to the reviewed papers published in the literature, the ALO algorithm is mostly utilized in solving various optimization problems. Presenting an overview and reviewing the ALO applications are the main aims of this review paper.

Ant Lion Optimizer: A Comprehensive Survey of Its Variants and Applications

In the rapidly expanding size and complexity of the electricity network, automatic generation control (AGC) is contemplated to be the most remarkable option for offering good quality electric power supply to the end users. An AGC system entails highly vigorous, competent and intelligent control technique to deliver a healthy power under stochastic nature of consumers’ power demand. Hence, in this paper, a hybrid fuzzy fractional order proportional integral-fractional order proportional derivative (FFOPI-FOPD) controller is proposed as a new expert control technique to tackle AGC profitably in isolated and interconnected multi-area power systems. A recently developed imperialist competitive algorithm (ICA) is utilized for the optimization of the output gains (KP/KP1/KI/KD) and other parameters such as order of integrator (λ) and differentiator (μ) of FFOPI-FOPD controller exercising integral of squared error criterion. The proposed technique is firstly implemented on 1-area thermal system, then to express its potential and extensibility, the work is extended to 2-area hydro-thermal and 3-area thermal power systems widespread in the literature. The eminence of the method is betokened by comparing the results with the various newly published control methodologies and FPI/FFOPI controller designed in the study via ICA in terms of minimum values of various error criteria and undershoots/overshoots/settling times of frequency and tie-line power deviations following a sudden load demand in an area. The sensitivity analysis substantiates that the suggested controller is robust and performs staunchly under the wide variations in the system parameters, random load pattern and in the company of physical constraints to produce more clean electricity.

A new optimized fuzzy FOPI-FOPD controller for automatic generation control of electric power systems

Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

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Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Realization of a fractional order fuzzy (FOF) PID controller with nonlinear gains.Performance evaluation of FOFPID controller for a binary distillation column.Comparison of this controller with its integer order counterpart.The proposed FOFPID controller was found superior in all aspects. Expert and intelligent control schemes have recently emerged out as a promising solution with robustness which can efficiently deal with the nonlinearities, along with various types of modelling uncertainties, present in different real world systems e.g. binary distillation column. This paper is an attempt to propose an intelligent control system which takes the form of a fractional order fuzzy proportional-integral-derivative (FOFPID) controller which is investigated as a solution to deal with the complex dynamic nature of the distillation column. The FOFPID controller is an extension of an existing formula based self tuning fuzzy proportional integral controller structure, which varies its gains at run time in accordance with the instantaneous error and rate of change of error. The FOFPID controller is a Takagi-Sugeno (TS) model based fuzzy adaptive controller comprising of non-integer order of integration and differentiation operators used in the controller. It has been observed that inclusion of non-integer order of the integration and differentiation operators made the controller scheme more robust. For the performance evaluation of the proposed scheme, the performance of FOFPID controller is compared with that of its integer order counterpart, a fuzzy proportional-integral-derivative (FPID) controller. The parameters of both the controllers were optimized for minimum integral of absolute error (IAE) using a bio-inspired global optimization algorithm, genetic algorithm (GA). Intensive LabVIEW? simulation studies were performed which included setpoint tracking with and without uncertainties, disturbance rejection, and noise suppression investigations. For testing the parameter uncertainty handling capability of the proposed controller, uncertain and time varying relative volatility and uncertain tray hydraulic constant were applied. Also, for the disturbance rejection studies, intensive simulations were conducted, which included two most common causes of disturbance i.e. variation in feed composition and variation in feed flow rate. All the simulation investigations clearly suggested that FOFPID controller provided superior performance over FPID controller for each case study i.e. setpoint tracking, disturbance rejection, noise suppression and parameter uncertainties.

A fractional order fuzzy PID controller for binary distillation column control

In this paper, a novel scheme is proposed for speed control of highly nonlinear hybrid electric vehicle (HEV) having an electronic throttle control system (ETCS) in the cascade control loop. Fractional order fuzzy PD (FOFPD) and fractional order fuzzy PI (FOFPI) nonlinear controllers are developed and used as primary and secondary controllers, respectively in the cascade control loop. These controllers are variable gain fuzzy controllers having an adaptive nature. Their corresponding integral counterpart, fuzzy PD and fuzzy PI controllers are realized by keeping integer order differentiator and integrator in FOFPD and FOFPI controllers. Further, fractional order PD (FOPD) and fractional order PI (FOPI) controllers are implemented by using non-integer order operators in conventional PD and PI controllers. Extensive simulations have been carried out using National Instruments software LabVIEW™ and its add-on tools such as control design and simulation toolkit to perform a comparative study for FOFPD, FPD, FOPD and PD as primary controller and FOFPI, FPI, FOPI and PI as secondary controller for setpoint tracking of speed of HEV. Multi objective genetic algorithm is used to optimize the gains of primary and secondary controllers for minimization of integral of absolute error (IAE), maximum overshoot and settling time. Performances of tuned controllers are further evaluated for tracking of speed profile, disturbance rejection and model uncertainty. It has been observed that combination of FOFPD and FOFPI controllers outperformed rest of the controllers in servo, regulatory and uncertain environment and demonstrate very robust behavior for speed control of HEV.

Robust speed control of hybrid electric vehicle using fractional order fuzzy PD and PI controllers in cascade control loop

In this paper, a self-tuned robust fractional order fuzzy proportional derivative (FO-FPD) controller is proposed for nonlinear active suspension system of a quarter car. Quarter car is a highly uncertain system as the number of riders or payload may vary. The control objective is to improve the level of ride comfort by minimizing the root mean square of vertical vibration acceleration (RMSVVA) of car body and maintaining hard constraints such as tracking force, ratio of tyre dynamic and static loads and suspension travel. The used FO-FPD controller is a nonlinear fuzzy logic controller having self-tuning capability, i.e. it changes its gains at runtime. The FO-FPD controller is realized using non-integer differentiator operator in FPD controller. The gains of FO-FPD and FPD controllers are tuned using Genetic Algorithm optimization method for a sinusoidal road surface. To evaluate the performances, extensive simulation studies were carried out and FO-FPD and FPD controllers were compared for different roads profiles such as sinusoidal, random and bump. It has been observed that FO-FPD and FPD controllers outperform passive suspension system in terms of RMSVVA and offer much better comfort ride while keeping all the hard constraint in the specified limits. Furthermore, FO-FPD controller demonstrated an excellent comfort ride, especially in uncertain environment, wherein it offered very robust behaviour as compared to FPD controller. Also, bonded-input and bounded-output stability conditions are established for overall closed loop control system by using small gain theorem.

Self-tuned robust fractional order fuzzy PD controller for uncertain and nonlinear active suspension system

In this paper, a robust fractional order fuzzy P + fuzzy I + fuzzy D (FOFP + FOFI + FOFD) controller is presented for a nonlinear and uncertain 2-link planar rigid manipulator. It is a nonlinear fuzzy controller with variable gains that makes it selfadjustable or adaptive in nature. The fractional order operators further make it more robust by providing additional degrees of freedom to the design engineer. The integer order counterpart, fuzzy P + fuzzy I + fuzzy D (FP + FI + FD) controller, for a comparative study, was realized by taking the integer value for the fractional order operators in FOFP + FOFI + FOFD controller. The performances of both the fuzzy controllers are evaluated for reference trajectory tracking and disturbance rejection with and without model uncertainty and measurement noise. Genetic algorithm was used to optimize the parameters of controller under study for minimum integral of absolute error. Simulation results demonstrated that FOFP + FOFI + FOFD controller show much better performance as compared to its counterpart FP + FI + FD controller in servo as well as the regulatory problem and in model uncertainty and noisy environment FOFP + FOFI + FOFD controller demonstrated more robust behavior as compared to the FP + FI + FD controller. For the developed controller bounded-input and bounded-output stability conditions are also developed using Small Gain Theorem.

A robust fractional order fuzzy P + fuzzy I + fuzzy D controller for nonlinear and uncertain system

A novel Nonlinear PI Controller (NPIC) has been proposed for effective control of flow process employing a sticky pneumatic control valve. The proposed control scheme has been inherited from a classical PI control structure with a difference that the integral gain has been varied in accordance with the instantaneous error and the rate of change of error. The tuning of controller has been carried out online using Differential Evolution algorithm. To evaluate the effectiveness of the proposed controller, a comparative study with the conventional PI controller has also been carried out for the setpoint tracking, disturbance rejection and robustness to parameter uncertainties on account of operating point change on a laboratory scale nonlinear flow process. Based on these intensive experimental evidences, it has been concluded that the NPIC performed far better than the conventional PI controller for all the case studies and suppressed effectively any stiction induced oscillations.

Puneet Mishra

Papers

A fractional order fuzzy PID controller for binary distillation column control

Robust speed control of hybrid electric vehicle using fractional order fuzzy PD and PI controllers in cascade control loop

Self-tuned robust fractional order fuzzy PD controller for uncertain and nonlinear active suspension system

A robust fractional order fuzzy P + fuzzy I + fuzzy D controller for nonlinear and uncertain system

An online tuned novel nonlinear PI controller for stiction compensation in pneumatic control valves.