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 A Novel Hybrid Metaheuristic Algorithm for Optimization of Construction Management Site Layout Planning

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 117 ◽  
Author(s):  
Doddy Prayogo ◽  
Min-Yuan Cheng ◽  
Yu-Wei Wu ◽  
A. A. N. Perwira Redi ◽  
Vincent F. Yu ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Optimization Technique ◽  
Solution Space ◽  
Metaheuristic Algorithm ◽  
Layout Planning ◽  
Symbiotic Organisms Search ◽  
Site Layout ◽  
Local Operators ◽  
Construction Site Layout ◽  
Symbiotic Organisms

Symbiotic organisms search (SOS) is a promising metaheuristic algorithm that has been studied recently by numerous researchers due to its capability to solve various hard and complex optimization problems. SOS is a powerful optimization technique that mimics the simulation of the typical symbiotic interactions among organisms in an ecosystem. This study presents a new SOS-based hybrid algorithm for solving the challenging construction site layout planning (CSLP) discrete problems. A new algorithm called the hybrid symbiotic organisms search with local operators (HSOS-LO) represents a combination of the canonical SOS and several local search mechanisms aimed at increasing the searching capability in discrete-based solution space. In this study, three CSLP problems that consist of single and multi-floor facility layout problems are tested, and the obtained results were compared with other widely used metaheuristic algorithms. The results indicate the robust performance of the HSOS-LO algorithm in handling discrete-based CSLP problems.

An enhanced symbiotic organisms search algorithm for design optimization of trusses with frequency constraints

2021 ◽  
pp. 136943322110262
Author(s):  
Mohammad H Makiabadi ◽  
Mahmoud R Maheri
Keyword(s):  
Standard Deviation ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Metaheuristic Algorithms ◽  
Minimum Mass ◽  
Multiple Frequency ◽  
Frequency Constraints ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms ◽  
Dynamic Frequency

An enhanced symbiotic organisms search (ESOS) algorithm is developed and presented. Modifications to the basic symbiotic organisms search algorithm are carried out in all three phases of the algorithm with the aim of balancing the exploitation and exploration capabilities of the algorithm. To verify validity and capability of the ESOS algorithm in solving general optimization problems, the CEC2014 set of 22 benchmark functions is first optimized and the results are compared with other metaheuristic algorithms. The ESOS algorithm is then used to optimize the sizing and shape of five benchmark trusses with multiple frequency constraints. The best (minimum) mass, mean mass, standard deviation of the mass, total number of function evaluations, and the values of frequency constraints are then compared with those of a number of other metaheuristic solutions available in the literature. It is shown that the proposed ESOS algorithm is generally more efficient in optimizing the shape and sizing of trusses with dynamic frequency constraints compared to other reported metaheuristic algorithms, including the basic symbiotic organisms search and its other recently proposed improved variants such as the improved symbiotic organisms search algorithm (ISOS) and modified symbiotic organisms search algorithm (MSOS).

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.

A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for global optimization problems

2019 ◽  
Vol 77 ◽  
pp. 567-583 ◽  
Author(s):  
Khoa H. Truong ◽  
Perumal Nallagownden ◽  
Zuhairi Baharudin ◽  
Dieu N. Vo
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

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.

A symbiotic organisms search algorithm-based design optimization of constrained multi-objective engineering design problems

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Deniz Ustun ◽  
Serdar Carbas ◽  
Abdurrahim Toktas
Keyword(s):  
Engineering Design ◽  
Multiple Objectives ◽  
Optimal Solutions ◽  
Design Problems ◽  
Symbiotic Organisms Search ◽  
Content Type ◽  
Multi Objective ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
Symbiotic Organisms

Purpose In line with computational technological advances, obtaining optimal solutions for engineering problems has become attractive research topics in various disciplines and real engineering systems having multiple objectives. Therefore, it is aimed to ensure that the multiple objectives are simultaneously optimized by considering them among the trade-offs. Furthermore, the practical means of solving those problems are principally concentrated on handling various complicated constraints. The purpose of this paper is to suggest an algorithm based on symbiotic organisms search (SOS), which mimics the symbiotic reciprocal influence scheme adopted by organisms to live on and breed within the ecosystem, for constrained multi-objective engineering design problems. Design/methodology/approach Though the general performance of SOS algorithm was previously well demonstrated for ordinary single objective optimization problems, its efficacy on multi-objective real engineering problems will be decisive about the performance. The SOS algorithm is, hence, implemented to obtain the optimal solutions of challengingly constrained multi-objective engineering design problems using the Pareto optimality concept. Findings Four well-known mixed constrained multi-objective engineering design problems and a real-world complex constrained multilayer dielectric filter design problem are tackled to demonstrate the precision and stability of the multi-objective SOS (MOSOS) algorithm. Also, the comparison of the obtained results with some other well-known metaheuristics illustrates the validity and robustness of the proposed algorithm. Originality/value The algorithmic performance of the MOSOS on the challengingly constrained multi-objective multidisciplinary engineering design problems with constraint-handling approach is successfully demonstrated with respect to the obtained outperforming final optimal designs.

scholarly journals Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization

2016 ◽  
Vol 3 (3) ◽  
pp. 226-249 ◽  
Author(s):  
Ghanshyam G. Tejani ◽  
Vimal J. Savsani ◽  
Vivek K. Patel
Keyword(s):  
Structural Design ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Search Space ◽  
Engineering Structures ◽  
Design Problems ◽  
Symbiotic Organisms Search ◽  
Structural Design Optimization ◽  
Symbiotic Organisms ◽  
Space Trusses

Abstract The symbiotic organisms search (SOS) algorithm is an effective metaheuristic developed in 2014, which mimics the symbiotic relationship among the living beings, such as mutualism, commensalism, and parasitism, to survive in the ecosystem. In this study, three modified versions of the SOS algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency. The basic SOS algorithm only considers benefit factors, whereas the proposed variants of the SOS algorithm, consider effective combinations of adaptive benefit factors and benefit factors to study their competence to lay down a good balance between exploration and exploitation of the search space. The proposed algorithms are tested to suit its applications to the engineering structures subjected to dynamic excitation, which may lead to undesirable vibrations. Structure optimization problems become more challenging if the shape and size variables are taken into account along with the frequency. To check the feasibility and effectiveness of the proposed algorithms, six different planar and space trusses are subjected to experimental analysis. The results obtained using the proposed methods are compared with those obtained using other optimization methods well established in the literature. The results reveal that the adaptive SOS algorithm is more reliable and efficient than the basic SOS algorithm and other state-of-the-art algorithms. Highlights Correlation between organisms, optimization and engineering. Adaptive symbiotic organisms search (SOS) algorithm is proposed. Implementation on structural design problems. Effective over other methods.

scholarly journals A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm for Ecosystem Sustainability

2021 ◽  
Author(s):  
Ajoze Abdulraheem Zubair ◽  
Shukor Bin Abd Razak ◽  
Md. Asri Bin Ngadi
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Scheduling Algorithm ◽  
Geometric Mean ◽  
Optimal Solution ◽  
Distinct Species ◽  
Scheduling Problems ◽  
Or Efficiency ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The modified SOS algorithm is developed to solve independent task scheduling problems. This paper proposes a modified symbiotic organisms search based scheduling algorithm for efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm's mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aimed to minimize the task execution time (Makespan), response, degree of imbalance and cost and improve the convergence speed for an optimal solution in an IaaS cloud. The performances of the proposed technique have been evaluated using a Cladism toolkit simulator, and the solutions are found to be better than the existing standard (SOS) technique and PSO.

Sign in / Sign up

Export Citation Format

Share Document