scholarly journals A modified controller design based on symbiotic organisms search optimization for desalination system

2019 ◽  
Vol 68 (5) ◽  
pp. 337-345 ◽  
Author(s):  
Natwar S. Rathore ◽  
V. P. Singh ◽  
Bui Duc Hong Phuc
Keyword(s):  
Fresh Water ◽  
Controller Design ◽  
Fitness Function ◽  
Time Domain Analysis ◽  
Multivariable Model ◽  
Symbiotic Organisms Search ◽  
Bee Colony ◽  
Squared Error ◽  
Search Optimization ◽  
Symbiotic Organisms

Abstract Fresh water demand is growing drastically in many parts of the world. Desalination of seawater, brackish water, and waste water is one solution to meet the demands of fresh water. Currently, reverse osmosis (RO) desalination process is one of the best methods for the desalination process. In this study, a modified controller design is proposed for RO desalination system based on symbiotic organisms search (SOS) algorithm. A multivariable model of RO desalination plant is considered for experimentation. The RO system considered here is first decoupled using a simplified decoupling process to obtain two non-interacting loops. Then, a proportional-integral-derivative controller with second order derivative (PID-DD) scheme based on SOS algorithm is proposed for each loop to find optimal control parameters of the RO system. To design the PID-DD controller for each loop, integral of squared error (ISE) is considered as fitness function. Four other state-of-the-art optimization algorithms, namely, teacher-learner-based-optimization (TLBO), differential evolution (DE), particle swarm optimization (PSO), and artificial bee colony (ABC), algorithms are also tested for the considered system. To show competitiveness of the proposed SOS-based PID-DD controller, a comparative study based on time domain analysis is performed. Results show the SOS-based PID-DD controller is superior to other PID-DD controllers.

scholarly journals Implementasi Metode Metaheuristik Symbiotic Organisms Search Dalam Penentuan Tata Letak Fasilitas Proyek Konstruksi Berdasarkan Jarak Tempuh Pekerja

2018 ◽  
Vol 19 (2) ◽  
pp. 103 ◽  
Author(s):  
Doddy Prayogo ◽  
Richard Antoni Gosno ◽  
Richard Evander ◽  
Sentosa Limanto
Keyword(s):  
Particle Swarm Optimization ◽  
Artificial Bee Colony ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Symbiotic Organisms Search ◽  
Bee Colony ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Symbiotic Organisms

Penelitian ini menyelidiki performa dari metode metaheuristik baru bernama symbiotic organisms search (SOS) dalam menentukan tata letak fasilitas proyek konstruksi yang optimal berdasarkan jarak tempuh pekerja. Dua buah studi kasus tata letak fasilitas digunakan untuk menguji akurasi dan konsistensi dari SOS. Sebagai tambahan, tiga metode metaheuristik lainnya, yaitu particle swarm optimization, artificial bee colony, dan teaching–learning-based optimization, digunakan sebagai pembanding terhadap algoritma SOS. Hasil simulasi mengindikasikan bahwa algoritma SOS lebih unggul serta memiliki karakteristik untuk menghasilkan titik konvergen lebih cepat jika dibandingkan dengan metode metaheuristik lainnya dalam proses optimasi tata letak fasilitas proyek konstruksi.

scholarly journals A Comparative Study on Bio-Inspired Algorithms in Layout Optimization of Construction Site Facilities

2018 ◽  
Vol 20 (2) ◽  
pp. 102 ◽  
Author(s):  
Doddy Prayogo ◽  
Jessica Chandra Sutanto ◽  
Hieronimus Enrico Suryo ◽  
Samuel Eric
Keyword(s):  
Optimization Problems ◽  
Transportation Cost ◽  
Layout Optimization ◽  
Symbiotic Organisms Search ◽  
Bee Colony ◽  
Site Layout ◽  
Critical Attention ◽  
Engineering Problems ◽  
Symbiotic Organisms ◽  
Planning Problems

A good arrangement of site layout on a construction project is a fundamental component of the project’s efficiency. Optimization on site layout is necessary in order to reduce the transportation cost of resources or personnel between facilities. Recently, the use of bio-inspired algorithms has received considerable critical attention in solving the engineering optimization problem. These methods have consistently provided better performance than traditional mathematical-based methods to a variety of engineering problems. This study compares the performance of particle swarm optimization (PSO), artificial bee colony (ABC), and symbiotic organisms search (SOS) algorithms in optimizing site layout planning problems. Three real-world case studies of layout optimization problems have been used in this study. The results show that SOS has a better performance in comparison to the other algorithms. Thus, this study provides useful insights to construction practitioners in the industry who are involved in dealing with optimization problems

scholarly journals Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm

2020 ◽  
Vol 10 (1) ◽  
pp. 171
Author(s):  
Nihad Dib ◽  
Umar Al-Sammarraie
Keyword(s):  
Optimal Design ◽  
High Speed ◽  
Search Algorithm ◽  
Fitness Function ◽  
Logic Gate ◽  
Optimization Methods ◽  
Optimization Method ◽  
Cmos Inverter ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

This paper investigates the optimal design of symmetric switching CMOS inverter using the Symbiotic Organisms Search (SOS) algorithm. SOS has been recently proposed as an effective evolutionary global optimization method that is inspired by the symbiotic interaction strategies between different organisms in an ecosystem. In SOS, the three common types of symbiotic relationships (mutualism, commensalism, and parasitism) are modeled using simple expressions, which are used to find the global minimum of the fitness function. Unlike other optimization methods, SOS has no parameters to be tuned, which makes it an attractive and easy-to-implement optimization method. Here, SOS is used to design a high speed symmetric switching CMOS inverter, which is considered the most fundamental logic gate. SOS results are compared to those obtained using several optimization methods, like particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and other ones, available in the literature. It is shown that the SOS is a robust straight-forward evolutionary algorithm that can compete with other well-known advanced methods.

scholarly journals An optimal production plan for cashew nuts community enterprise using metaheuristic algorithms

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Production Cycle ◽  
Production Plan ◽  
Data Set ◽  
Optimal Production ◽  
Symbiotic Organisms Search ◽  
Holding Costs ◽  
Predictive Skill ◽  
Bee Colony ◽  
Ant Lion Optimization ◽  
Symbiotic Organisms

The proper production plan plays an important role in the cashew nuts market enterprise in order to reduce cost. This study aims to find the optimal production plan for cashew nuts using ant lion optimization (ALO), symbiotic organisms search (SOS), particle swarm optimization (PSO) and artificial bee colony algorithm (ABC). The novel objective function is introduced in this study. Three input data set, including production cost, holding cost and inventory quantity are investigated. The experiment cases consist of the frequency of production cycle time in January, February and March, respectively. As a results, four algorithms are available to estimate not only the proper production plan of cashew nuts but also an ability in reducing the inventory and the holding costs. In summary, the ALO algorithm provides better predictive skill than others for the cashew nuts production plan with the lowest RMSE value of 0.0913.

Sign in / Sign up

Export Citation Format

Share Document