scholarly journals Optimization of resource leveling problem under multiple objective criteria using a symbiotic organisms search

2019 ◽  
Vol 21 (1) ◽  
pp. 43-49 ◽  
Author(s):  
Doddy Prayogo ◽  
Christianto Tirta Kusuma
Keyword(s):  
Particle Swarm Optimization ◽  
Resource Management ◽  
Objective Function ◽  
Multiple Objective ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Cost Overruns ◽  
Symbiotic Organisms Search ◽  
Resource Leveling ◽  
Symbiotic Organisms

Bad scheduling and resource management can cause delays or cost overruns. Optimization in solving resource leveling is necessary to avoid those problems. Several objective criteria are used to solve resource leveling. Each of them has the same objective, which is to reduce the fluctuation of resource demand of the project. This study compares the performance of particle swarm optimization (PSO) and symbiotic organisms search (SOS) in solving resource leveling problems using separate objective functions in order to find which one produces a better solution. The results show that SOS produced a better solution than PSO, and one objective function is better in solving resource leveling than the others.

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 Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm for Solving Optimal Reactive Power Dispatch Problem

2020 ◽  
Vol 3 (1) ◽  
pp. 12-21
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Symbiotic Organisms Search ◽  
Power Dispatch ◽  
Fitness Value ◽  
Symbiotic Organisms

In this work Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm (HGPSOS) has been done for solving the power dispatch problem. Genetic particle swarm optimization problem has been hybridized with Symbiotic organisms search (SOS) algorithm to solve the problem. Genetic particle swarm optimization algorithm is formed by combining the Particle swarm optimization algorithm (PSO) with genetic algorithm (GA).  Symbiotic organisms search algorithm is based on the actions between two different organisms in the ecosystem- mutualism, commensalism and parasitism. Exploration process has been instigated capriciously and every organism specifies a solution with fitness value.  Projected HGPSOS algorithm improves the quality of the search.  Proposed HGPSOS algorithm is tested in IEEE 30, bus test system- power loss minimization, voltage deviation minimization and voltage stability enhancement has been attained.

Particle Swarm Methodologies for Engineering Design Optimization

2009 ◽  
Author(s):  
Singiresu S. Rao ◽  
Kiran K. Annamdas
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Multiple Objective ◽  
Optimization Approach ◽  
Objective Functions ◽  
Mixed Design ◽  
Swarm Optimization ◽  
Design Variables ◽  
Mixed Design Variables

Particle swarm methodologies are presented for the solution of constrained mechanical and structural system optimization problems involving single or multiple objective functions with continuous or mixed design variables. The particle swarm optimization presented is a modified particle swarm optimization approach, with better computational efficiency and solution accuracy, is based on the use of dynamic maximum velocity function and bounce method. The constraints of the optimization problem are handled using a dynamic penalty function approach. To handle the discrete design variables, the closest discrete approach is used. Multiple objective functions are handled using a modified cooperative game theory approach. The applicability and computational efficiency of the proposed particle swarm optimization approach are demonstrated through illustrate examples involving single and multiple objectives as well as continuous and mixed design variables. The present methodology is expected to be useful for the solution of a variety of practical engineering design optimization problems.

Application of TVAC-PSO for Optimal Taiwan Power Dispatch with Carbon Emission Control

2014 ◽  
Vol 681 ◽  
pp. 270-274
Author(s):  
Whei Min Lin ◽  
Chia Sheng Tu ◽  
Ming Tang Tsai ◽  
Fu Sheng Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Operating Cost ◽  
Particle Swarm ◽  
Time Varying ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Power Dispatch ◽  
Effectiveness And Efficiency ◽  
Dynamic Economic Dispatch

This paper integrated the Particle Swarm Optimization and time-varying inertia weight model to propose a Time-Varying Acceleration Coefficients in Particle Swarm Optimization (TVAC-PSO) for dealing with the emission-constrained dynamic economic dispatch (ED) problems. The objective function of Taiwan power dispatch with emission considerations includes the sub-objective functions of operating cost and emissions. The objective function of emissions is estimated by IPCC. TVAC-PSO is used to find the objective function under the operational and system’s constraints. The effectiveness and efficiency of the TVAC-PSO are demonstrated by using a simplified Taiwan power system. It can also provide an interactive mechanism to adjust the emission permit.

scholarly journals OPTIMASI TOPOLOGI DAN UKURAN PENAMPANG STRUKTUR RANGKA BATANG BAJA DENGAN METODE METAHEURISTIK

2019 ◽  
Vol 6 (1) ◽  
pp. 33-42
Author(s):  
Ricky Agusta Hartono
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Symbiotic Organisms Search ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Symbiotic Organisms ◽  
Evolution Teaching

Optimasi topologi dari struktur rangka batang baja memberikan hasil yang lebih optimal dibandingkan optimasi ukuran penampang karena batang dan nodes yang tidak berguna pada struktur dapat dihilangkan. Fungsi objektif dari algoritma metaheuristik adalah untuk meminimalkan massa struktur rangka batang baja terhadap constraints statis dan dinamis berdasarkan studi kasus dan spesifikasi bangunan baja struktural Indonesia, SNI 1729:2015. Empat algoritma yang digunakan pada studi ini adalah: Particle Swarm Optimization, Differential Evolution, Teaching-Learning-Based Optimization, dan Symbiotic Organisms Search. Keempat algoritma tersebut diuji pada studi kasus 24-bar truss. Performa dari algoritma diukur dari lima kriteria massa, yaitu: massa terbaik, terburuk, rata-rata, standar deviasi, dan median dari struktur rangka batang baja. Hasil penelitian menunjukkan SOS menunjukkan performa terbaik pada studi kasus 24-bar truss.

OPTIMASI BIAYA PONDASI SETEMPAT DENGAN METODE METAHEURISTIK

2020 ◽  
Vol 7 (2) ◽  
pp. 47-52
Author(s):  
Vania Regina Husada ◽  
Ferdian Nathanael ◽  
Doddy Prayogo ◽  
Yudas Tadeus Teddy Susanto
Keyword(s):  
Particle Swarm Optimization ◽  
Bearing Capacity ◽  
Limit State ◽  
Ultimate Limit State ◽  
Swarm Optimization ◽  
Symbiotic Organisms Search ◽  
Serviceability Limit State ◽  
Capacity Theory ◽  
Symbiotic Organisms ◽  
Ultimate Limit

Pondasi adalah bagian dari struktur bangunan yang berfungsi meneruskan beban struktur atas ke lapisan tanah dengan aman. Sementara pondasi dangkal digunakan apabila lapisan tanah keras terletak dekat dengan permukaan tanah. Untuk mendapatkan hasil desain pondasi dangkal yang optimal, terdapat tiga kriteria penting yang harus diperhatikan yaitu Ultimate Limit State (ULS), Serviceability Limit State (SLS), dan ekonomis. Sehingga, penggunaan metode optimasi yang baik akan membantu menghasilkan dimensi pondasi yang optimal dan ekonomis namun tetap memenuhi syarat aman. Penelitian-penelitian sebelumnya mengindikasikan bahwa metode metaheuristik dapat digunakan sebagai alternatif yang mampu menyelesaikan permasalahan optimasi yang ada. Oleh karena itu, penelitian ini menggunakan metode metaheuristik Particle Swarm Optimization (PSO) dan Symbiotic Organisms Search (SOS) untuk menyelesaikan permasalahan optimasi pondasi dangkal. Pada penelitian ini, optimasi pondasi dangkal dilakukan terhadap pondasi setempat untuk studi kasus bangunan dua lantai. PSO dan SOS bekerja untuk menemukan solusi dimensi pondasi setempat yang diharapkan dapat memiliki biaya konstruksi terendah dan dibatasi oleh constraint dari SNI 8460:2017, SNI 2847:2013, dan bearing capacity theory. Hasil penelitian menunjukkan bahwa metode metaheuristik mampu menemukan dimensi pondasi dangkal yang optimal untuk masing-masing studi kasus. Selain itu, dapat dilihat apabila algoritma SOS memiliki performa yang lebih baik dari PSO.

scholarly journals Different Object Functions for SWIPT Optimization by SADDE and APSO

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1340
Author(s):  
Wei Chien ◽  
Chien-Ching Chiu ◽  
Po-Hsiang Chen ◽  
Yu-Ting Cheng ◽  
Eng Hock Lim ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Antenna Array ◽  
Millimeter Wave ◽  
Bit Error Rate ◽  
Weighting Factor ◽  
Multiple Objective ◽  
The Novel ◽  
Object Function ◽  
Swarm Optimization ◽  
Multiple Receivers

Multiple objective function with beamforming techniques by algorithms have been studied for the Simultaneous Wireless Information and Power Transfer (SWIPT) technology at millimeter wave. Using the feed length to adjust the phase for different objects of SWIPT with Bit Error Rate (BER) and Harvesting Power (HP) are investigated in the broadband communication. Symmetrical antenna array is useful for omni bearing beamforming adjustment with multiple receivers. Self-Adaptive Dynamic Differential Evolution (SADDE) and Asynchronous Particle Swarm Optimization (APSO) are used to optimize the feed length of the antenna array. Two different object functions are proposed in the paper. The first one is the weighting factor multiplying the constraint BER and HP plus HP. The second one is the constraint BER multiplying HP. Simulations show that the first object function is capable of optimizing the total harvesting power under the BER constraint and APSO can quickly converges quicker than SADDE. However, the weighting for the final object function requires a pretest in advance, whereas the second object function does not need to set the weighting case by case and the searching is more efficient than the first one. From the numerical results, the proposed criterion can achieve the SWIPT requirement. Thus, we can use the novel proposed criterion (the second criterion) to optimize the SWIPT problem without testing the weighting case by case.

Assessing the performances of recent global search algorithms using analytic objective functions and seismic optimization problems

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R767-R781 ◽  
Author(s):  
Mattia Aleardi ◽  
Silvio Pierini ◽  
Angelo Sajeva
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Model Space ◽  
Global Search ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Fireworks Algorithm ◽  
Highly Nonlinear ◽  
Whale Optimization

We have compared the performances of six recently developed global optimization algorithms: imperialist competitive algorithm, firefly algorithm (FA), water cycle algorithm (WCA), whale optimization algorithm (WOA), fireworks algorithm (FWA), and quantum particle swarm optimization (QPSO). These methods have been introduced in the past few years and have found very limited or no applications to geophysical exploration problems thus far. We benchmark the algorithms’ results against the particle swarm optimization (PSO), which is a popular and well-established global search method. In particular, we are interested in assessing the exploration and exploitation capabilities of each method as the dimension of the model space increases. First, we test the different algorithms on two multiminima and two convex analytic objective functions. Then, we compare them using the residual statics corrections and 1D elastic full-waveform inversion, which are highly nonlinear geophysical optimization problems. Our results demonstrate that FA, FWA, and WOA are characterized by optimal exploration capabilities because they outperform the other approaches in the case of optimization problems with multiminima objective functions. Differently, QPSO and PSO have good exploitation capabilities because they easily solve ill-conditioned optimizations characterized by a nearly flat valley in the objective function. QPSO, PSO, and WCA offer a good compromise between exploitation and exploration.

Comparison of SVC and TCSC Installation in Transmission Line with Loss Minimization and Cost of Installation via Particle Swarm Optimization

2015 ◽  
Vol 785 ◽  
pp. 495-499
Author(s):  
Siti Amely Jumaat ◽  
Ismail Musirin
Keyword(s):  
Particle Swarm Optimization ◽  
Transmission Line ◽  
Objective Function ◽  
Transmission Loss ◽  
Particle Swarm ◽  
Static Var Compensator ◽  
Loss Minimization ◽  
Swarm Optimization ◽  
The Cost ◽  
Thyristor Controlled Series Compensator

The paper presents a comparison of performance Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) with objective function to minimize the transmission loss, improve the voltage and monitoring the cost of installation. Simulation performed on standard IEEE 30-Bus RTS and indicated that EPSO a feasible to achieve the objective function.

scholarly journals Recursive Neural Networks Based on PSO for Image Parsing

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Guo-Rong Cai ◽  
Shui-Li Chen
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
State Of The Art ◽  
Pso Algorithm ◽  
Training Algorithm ◽  
Image Parsing ◽  
Swarm Optimization ◽  
Recursive Neural Networks ◽  
Parsing Algorithm

This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO) and Recursive Neural Networks (RNNs). State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters. However, this could cause problems due to the nondifferentiable objective function. In order to solve this problem, the PSO algorithm has been employed to tune the weights of RNN for minimizing the objective. Experimental results obtained on the Stanford background dataset show that our PSO-based training algorithm outperforms traditional RNN, Pixel CRF, region-based energy, simultaneous MRF, and superpixel MRF.

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