Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization

2018 ◽  
Vol 75 (5) ◽  
pp. 2395-2422 ◽  
Author(s):  
Mohammad Shehab ◽  
Ahamad Tajudin Khader ◽  
Makhlouf Laouchedi ◽  
Osama Ahmad Alomari
Keyword(s):  
Numerical Optimization ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Global Numerical Optimization

Multiloop FOPID Controller Design for TITO Process Using Evolutionary Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 117-130 ◽  
Author(s):  
Lakshmanaprabu S.K. ◽  
Najumnissa Jamal D. ◽  
Sabura Banu U.
Keyword(s):  
Evolutionary Algorithm ◽  
Pid Controller ◽  
Controller Design ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Internal Model Control ◽  
Model Control ◽  
Fractional Order Pid

In this article, the tuning of multiloop Fractional Order PID (FOPID) controller is designed for Two Input Two Output (TITO) processes using an evolutionary algorithm such as the Genetic algorithm (GA), the Cuckoo Search algorithm (CS) and the Bat Algorithm (BA). The control parameters of FOPID are obtained using GA, CS, and BA for minimizing the integral error criteria. The main objective of this article is to compare the performance of the GA, CS, and BA for the multiloop FOPID controller problem. The integer order internal model control based PID (IMC-PID) controller is designed using the GA and the performance of the IMC-PID controller is compared with the FOPID controller scheme. The simulation results confirm that BA offers optimal controller parameter with a minimum value of IAE, ISE, ITAE with faster settling time.

A Survey of Computational Intelligence Algorithms and Their Applications

2020 ◽  
pp. 1886-1929
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Computational Intelligence ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Evolutionary Strategy ◽  
General Process ◽  
Fireworks Algorithm ◽  
Starting Point ◽  
New Algorithms

This chapter subscribes in the framework of an analytical study about the computational intelligence algorithms. These algorithms are numerous and can be classified in two great families: evolutionary algorithms (genetic algorithms, genetic programming, evolutionary strategy, differential evolutionary, paddy field algorithm) and swarm optimization algorithms (particle swarm optimisation PSO, ant colony optimization (ACO), bacteria foraging optimisation, wolf colony algorithm, fireworks algorithm, bat algorithm, cockroaches colony algorithm, social spiders algorithm, cuckoo search algorithm, wasp swarm optimisation, mosquito optimisation algorithm). We have detailed each algorithm following a structured organization (the origin of the algorithm, the inspiration source, the summary, and the general process). This paper is the fruit of many years of research in the form of synthesis which groups the contributions proposed by various researchers in this field. It can be the starting point for the designing and modelling new algorithms or improving existing algorithms.

A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks

2017 ◽  
Vol 8 (1) ◽  
pp. 50-62 ◽  
Author(s):  
Ramadan Babers ◽  
Aboul Ella Hassanien
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Krill Herd ◽  
Artificial Fish Swarm ◽  
Lion Optimization Algorithm

In last few years many approaches have been proposed to detect communities in social networks using diverse ways. Community detection is one of the important researches in social networks and graph analysis. This paper presents a cuckoo search optimization algorithm with Lévy flight for community detection in social networks. Experimental on well-known benchmark data sets demonstrates that the proposed algorithm can define the structure and detect communities of complex networks with high accuracy and quality. In addition, the proposed algorithm is compared with some swarms algorithms including discrete bat algorithm, artificial fish swarm, discrete Krill Herd, ant lion algorithm and lion optimization algorithm and the results show that the proposed algorithm is competitive with these algorithms.

scholarly journals Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms

Algorithms ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 54 ◽  
Author(s):  
Juan Templos-Santos ◽  
Omar Aguilar-Mejia ◽  
Edgar Peralta-Sanchez ◽  
Raul Sosa-Cortez
Keyword(s):  
Search Algorithm ◽  
Permanent Magnet Synchronous Motor ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Parameter Tuning ◽  
Cuckoo Search Algorithm ◽  
Flower Pollination Algorithm ◽  
Speed Regulation ◽  
Pi Controllers ◽  
Nature Inspired Algorithms

This article focuses on the optimal gain selection for Proportional Integral (PI) controllers comprising a speed control scheme for the Permanent Magnet Synchronous Motor (PMSM). The gains calculation is performed by means of different algorithms inspired by nature, which allows improvement of the system performance in speed regulation tasks. For the tuning of the control parameters, five optimization algorithms are chosen: Bat Algorithm (BA), Biogeography-Based Optimization (BBO), Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA) and Sine-Cosine Algorithm (SCA). Finally, for purposes of efficiency assessment, two reference speed profiles are introduced, where an acceptable PMSM performance is attained by using the proposed PI controllers tuned by nature inspired algorithms.

Hybridizing harmony search algorithm with cuckoo search for global numerical optimization

2014 ◽  
Vol 20 (1) ◽  
pp. 273-285 ◽  
Author(s):  
Gai-Ge Wang ◽  
Amir H. Gandomi ◽  
Xiangjun Zhao ◽  
Hai Cheng Eric Chu
Keyword(s):  
Numerical Optimization ◽  
Search Algorithm ◽  
Harmony Search ◽  
Cuckoo Search ◽  
Harmony Search Algorithm ◽  
Global Numerical Optimization
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