PI controller optimization for a heat exchanger through metaheuristic Bat Algorithm, Particle Swarm Optimization, Flower Pollination Algorithm and Cuckoo Search Algorithm

Abstract Among the data clustering algorithms, the k-means (KM) algorithm is one of the most popular clustering techniques because of its simplicity and efficiency. However, KM is sensitive to initial centers and it has a local optima problem. The k-harmonic means (KHM) clustering algorithm solves the initialization problem of the KM algorithm, but it also has a local optima problem. In this paper, we develop a new algorithm for solving this problem based on a modified version of particle swarm optimization (MPSO) algorithm and KHM clustering. In the proposed algorithm, MPSO is equipped with the cuckoo search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence, and escape from local optima. MPSO updates the positions of particles based on a combination of global worst, global best with personal worst, and personal best to dynamically be used in each iteration of the MPSO. The experimental result on eight real-world data sets and two artificial data sets confirms that this modified version is superior to KHM and the regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy, and correctness while markedly improving the processing time.

Download Full-text

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

Algorithms ◽

10.3390/a12030054 ◽

2019 ◽

Vol 12 (3) ◽

pp. 54 ◽

Cited By ~ 5

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.

Download Full-text

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

Foundations of Computing and Decision Sciences ◽

10.1515/fcds-2016-0006 ◽

2016 ◽

Vol 41 (2) ◽

pp. 99-121 ◽

Cited By ~ 2

Author(s):

Asgarali Bouyer

Keyword(s):

Particle Swarm Optimization ◽

Search Algorithm ◽

Particle Swarm ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Fast Convergence ◽

Experimental Result ◽

Swarm Optimization ◽

Local Optima ◽

Harmonic Means

AbstractAmong the data clustering algorithms, k-means (KM) algorithm is one of the most popular clustering techniques due to its simplicity and efficiency. However, k-means is sensitive to initial centers and it has the local optima problem. K-harmonic-means (KHM) clustering algorithm solves the initialization problem of k-means algorithm, but it also has local optima problem. In this paper, we develop a new algorithm for solving this problem based on an improved version of particle swarm optimization (IPSO) algorithm and KHM clustering. In the proposed algorithm, IPSO is equipped with Cuckoo Search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence and escape from local optima. IPSO updates positions of particles based on a combination of global worst, global best with personal worst and personal best to dynamically be used in each iteration of the IPSO. The experimental result on five real-world datasets and two artificial datasets confirms that this improved version is superior to k-harmonic means and regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy and correctness while markedly improving the processing time.

Download Full-text

A Modified Cuckoo Search Algorithm for Data Clustering

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2022010101 ◽

2022 ◽

Vol 13 (1) ◽

pp. 0-0

Keyword(s):

Particle Swarm Optimization ◽

Search Algorithm ◽

Particle Swarm ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Swarm Optimization ◽

Bee Colony ◽

Nature Inspired Algorithms ◽

Benchmark Test Functions ◽

Optimal Cluster

Clustering of data is one of the necessary data mining techniques, where similar objects are grouped in the same cluster. In recent years, many nature-inspired based clustering techniques have been proposed, which have led to some encouraging results. This paper proposes a Modified Cuckoo Search (MoCS) algorithm. In this proposed work, an attempt has been made to balance the exploration of the Cuckoo Search (CS) algorithm and to increase the potential of the exploration to avoid premature convergence. This algorithm is tested using fifteen benchmark test functions and is proved as an efficient algorithm in comparison to the CS algorithm. Further, this method is compared with well-known nature-inspired algorithms such as Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Particle Swarm Optimization with Age Group topology (PSOAG) and CS algorithm for clustering of data using six real datasets. The experimental results indicate that the MoCS algorithm achieves better results as compared to other algorithms in finding optimal cluster centers.

Download Full-text