Construction Projects Problems Optimization Using PSO and GSA

Construction projects are a combination of high complicated procedures that rarely go with the plan. The greatest dangers projects are the construction since it linked with an extraordinary amount of ambiguity and threat and that because of the business activities nature, procedures, and the outside surroundings. This paper investigates the problems during the pre-construction phase and the optimal solution for this problem by using to algorithm, partial swarm and Gravitational search algorithm. The results show that the construction problems have a severe effect on both time and cost and these problems must be treated immediately and this requires sophisticated techniques by using computer science. GSA and PSO are both used and show excellent results in solving these problems, the GSA algorithm shows better results in both the velocity is taken to find the solutions and in the accuracy. PSO is still a good technique in finding the solution and their future recommendation in making an expert system to find the solution more than one project and their interdependency.

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Direct Gravitational Search Algorithm for Global Optimisation Problems

East Asian Journal on Applied Mathematics ◽

10.4208/eajam.030915.210416a ◽

2016 ◽

Vol 6 (3) ◽

pp. 290-313 ◽

Cited By ~ 2

Author(s):

Ahmed F. Ali ◽

Mohamed A. Tawhid

Keyword(s):

Heuristic Algorithm ◽

Hybrid Algorithm ◽

Search Algorithm ◽

Direct Method ◽

Gravitational Search Algorithm ◽

Optimal Solution ◽

Slow Convergence ◽

Gravitational Search ◽

Law Of Gravity ◽

Number Of Iterations

AbstractA gravitational search algorithm (GSA) is a meta-heuristic development that is modelled on the Newtonian law of gravity and mass interaction. Here we propose a new hybrid algorithm called the Direct Gravitational Search Algorithm (DGSA), which combines a GSA that can perform a wide exploration and deep exploitation with the Nelder-Mead method, as a promising direct method capable of an intensification search. The main drawback of a meta-heuristic algorithm is slow convergence, but in our DGSA the standard GSA is run for a number of iterations before the best solution obtained is passed to the Nelder-Mead method to refine it and avoid running iterations that provide negligible further improvement. We test the DGSA on 7 benchmark integer functions and 10 benchmark minimax functions to compare the performance against 9 other algorithms, and the numerical results show the optimal or near optimal solution is obtained faster.

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Risk Response Selection in Construction Projects

Civil Engineering Journal ◽

10.28991/cej-030950 ◽

2018 ◽

Vol 3 (12) ◽

pp. 1208 ◽

Cited By ~ 2

Author(s):

Hafeth I. Naji ◽

Rouwaida Hussein Ali

Keyword(s):

Risk Management ◽

Optimal Strategy ◽

Construction Projects ◽

Response Selection ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Response Strategy ◽

Risk Response ◽

Gravitational Search ◽

The Cost

Risk and its management is important for the success of the project, the risk management, which encompassed of planning, identification, analysis, and response has an important phase, which is risk response and it should not be undermined, as its success going to the projects the capability to overcome the uncertainty and thus an effective tool in project risk management, risk response used the collective information in the analysis stage and in order to take decision how to improve the possibility to complete the project within time, cost and performance. This stage work on preparing the response to the main risks and appoint the people who are responsible for each response. When it's needed risk response may be started in quantitative analysis stage and the repetition may be possible between the analysis and risk response stage. The aim of this research is to provide a methodology to make the plane for unexpected events and control uncertain situations and identify the reason for risk response failure and to respond to risk successfully by using the optimization method to select the best strategy. The methodology of this research divided into four parts, the first part main object is to find the projects whose risk response is failed, the second part includes the reasons for risk response Failure, the third part includes finding the most important risks generated from risk response that leads to increasing the cost of construction projects, the fourth part of the management system is selecting the optimal risk response strategy. An optimization model was used to select the optimal strategy to treat the risk by using Serval constraints such as the cost of the project, time of the project, Gravitational Search Algorithm and particle swarm used. The result of the risk response selection shows that The investment (contractor, bank) strategy shows a very good strategy as it saves the cost about 30%, while the Mitigate (pay for advances with interest 0. 1) Strategy show saving the cost 40% and giving land to contractors show saving the cost 40% finally the BIM strategy show saving the cost 25%. The risk response is an important part and should give a great attention and it must be used sophisticated method to select the optimal strategy, the two techniques both show high efficiency in selecting the strategy but Gravitational Search Algorithm show better performance.

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Evolutionary correlated gravitational search algorithm (ECGS) with genetic optimized Hopfield neural network (GHNN) – A hybrid expert system for diagnosis of diabetes

Measurement ◽

10.1016/j.measurement.2018.12.083 ◽

2019 ◽

Vol 145 ◽

pp. 551-558 ◽

Cited By ~ 3

Author(s):

J. Jayashree ◽

S. Ananda Kumar

Keyword(s):

Neural Network ◽

Expert System ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Hopfield Neural Network ◽

Gravitational Search

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Solving Two–Sided Assembly Line Balancing Problem via Gravitational Search Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.697.450 ◽

2014 ◽

Vol 697 ◽

pp. 450-455 ◽

Cited By ~ 1

Author(s):

Yi Wu ◽

Qiu Hua Tang ◽

Li Ping Zhang ◽

Zi Xiang Li ◽

Xiao Jun Cao

Keyword(s):

Assembly Line ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Assembly Line Balancing ◽

Optimal Solution ◽

High Volume ◽

Line Balancing ◽

Task Sequence ◽

Gravitational Search ◽

Assembly Line Balancing Problem

Two-sided assembly lines are widely applied in plants for producing large-sized high volume products, such as trucks and buses. Since the two-sided assembly line balancing problem (TALBP) is NP-hard, it is difficult to get an optimal solution in polynomial time. Therefore, a novel swarm based heuristic algorithm named gravitational search algorithm (GSA) is proposed to solve this problem with the objective of minimizing the number of mated-stations and the number of stations simultaneously. In order to apply GSA to solving the TALBP, an encoding scheme based on the random-keys method is used to convert the continuous positions of the GSA into the discrete task sequence. In addition, a new decoding scheme is implemented to decrease the idle time related to sequence-dependent finish time of tasks. The corresponding experiment results demonstrate that the proposed algorithm outperforms other well-known algorithms.

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Non-Convex Economic Dispatch with Prohibited Operating Zones through Gravitational Search Algorithm

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v5i6.pp1234-1244 ◽

2015 ◽

Vol 5 (6) ◽

pp. 1234

Author(s):

Prakash Kumar Hota ◽

Nakul Charan Sahu

Keyword(s):

Search Algorithm ◽

Gravitational Search Algorithm ◽

Optimal Solution ◽

Inequality Constraints ◽

New Approach ◽

Prohibited Operating Zones ◽

Optimal Power ◽

Equality And Inequality Constraints ◽

Simulation Results ◽

Gravitational Search

This paper presents a new approach to the solution of optimal power generation for economic load dispatch (ELD) using gravitational search algorithm (GSA) when all the generators include valve point effects and some/all of the generators have prohibited operating zones. In this paper a gravitational search algorithm is suggested that deals with equality and inequality constraints in ELD problems. A constraint treatment mechanism is also discussed to accelerate the optimization process<strong>. </strong>To verify the robustness and superiority of the proposed GSA based approach, a practical sized 40-generators case with valve point effects and prohibited operating zones is considered. The simulation results reveal that the proposed GSA approach ensures convergence within an acceptable execution time and provides highly optimal solution as compared to the results obtained from well established heuristic optimization approaches.

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Gravitational Search Algorithm

Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-9644-0.ch027 ◽

2016 ◽

pp. 700-750 ◽

Cited By ~ 1

Author(s):

Hossein Nezamabadi-Pour ◽

Fatemeh Barani

Keyword(s):

Optimization Problems ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Processing System ◽

Optimal Solution ◽

Basic Algorithm ◽

Comprehensive Survey ◽

Gravitational Search ◽

Improved Performance ◽

Short Time

During the last decades, several metaheuristics have been developed to solve complex engineering optimization problems which most of them have been inspired by natural phenomena and swarm behaviors. Metaheuristics are the most selected techniques to find optimal solution intelligently in many areas of scheduling, space allocation, decision making, pattern recognition, document clustering, control objectives, image processing, system and filter modeling, etc. These algorithms have promised better solutions in single and multi-objective optimization. Gravitational search algorithm (GSA) is one of the recent created metaheuristic search algorithms, which is inspired by the Newtonian laws of gravity and motion. GSA was first proposed by Rashedi et al. and in the short time it became popular among the scientific community and researchers resulting in a lot of variants of the basic algorithm with improved performance. This chapter book presents a detailed review of the basic concepts of GSA and a comprehensive survey of its advanced versions. We propose a number of suggestions to the GSA community that can be undertaken to help move the area forward.

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Improvement Method of Fuzzy Geographically Weighted Clustering using Gravitational Search Algorithm

Jurnal Ilmu Komputer dan Informasi ◽

10.21609/jiki.v11i1.580 ◽

2018 ◽

Vol 11 (1) ◽

pp. 10

Author(s):

Setia Pramana ◽

Imam Habib Pamungkas

Keyword(s):

Search Algorithm ◽

Gravitational Search Algorithm ◽

Optimal Solution ◽

R Package ◽

Weighted Clustering ◽

Novel Approach ◽

Local Optimal Solution ◽

Improvement Method ◽

Gravitational Search ◽

Cluster Quality

Geo-demographic analysis (GDA) is a useful method to analyze information based on location, utilizing several spatial analysis explicitly. One of the most efficient and commonly used method is Fuzzy Geographically Weighted Clustering (FGWC). However, it has a limitation in obtaining local optimal solution in the centroid initialization. A novel approach integrating Gravitational Search Algorithm (GSA) with FGWC is proposed to obtain global optimal solution leading to better cluster quality. Several cluster validity indexes are used to compare the proposed methods with the FGWC using other optimization approaches. The study shows that the hybrid method FGWC-GSA provides better cluster quality. Furthermore, the method has been implemented in R package spatialClust.

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