Direct Gravitational Search Algorithm for Global Optimisation Problems

2016 ◽  
Vol 6 (3) ◽  
pp. 290-313 ◽  
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.

Gravitational Search Algorithm Optimization for PID Controller Tuning in Waste-water Treatment Process

2015 ◽  
Vol 73 (3) ◽  
Author(s):  
Mohamad Saiful Islam Aziz ◽  
Sophan Wahyudi Nawawi ◽  
Shahdan Sudin ◽  
Norhaliza Abdul Wahab ◽  
Mahdi Faramarzi ◽  
...  
Keyword(s):  
Pid Controller ◽  
Treatment Process ◽  
Search Algorithm ◽  
Waste Water Treatment ◽  
Gravitational Search Algorithm ◽  
Water Treatment Process ◽  
Pid Controller Tuning ◽  
Gravitational Search ◽  
Law Of Gravity

This paper presents a new approach of optimization technique in the controller parameter tuning for waste-water treatment process (WWTP) application. In the case study of WWTP, PID controller is used to control substrate (S) and dissolved oxygen (DO) concentration level. Too many parameters that need to be controlled make the system becomes complicated. Gravitational Search Algorithm (GSA) is used as the main method for PID controller tuning process. GSA is based on Newton's Law of Gravity and mass interaction. In this algorithm, the searcher agents survey the masses that interact with each other using law of gravity and law of motion. For WWTP system, the activated sludge reactor is used and this system is multi-input multi-output (MIMO) process. MATLAB is used as the platform to perform the simulation, where this optimization is compared to other established optimization method such as the Particle Swarm Optimization (PSO) to determine whether GSA has better features compared to PSO or vice-versa. Based on this case-study, the results show that transient response of GSA-PID was 20%-30% better compared to transient response of the PSO-PID controller.

Construction Projects Problems Optimization Using PSO and GSA

2020 ◽  
Vol 897 ◽  
pp. 147-151
Author(s):  
Naji Mutar Sahib ◽  
Abtehaj Hussein ◽  
Suha Falih ◽  
Hafeth I. Naji
Keyword(s):  
Expert System ◽  
Computer Science ◽  
Construction Projects ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimal Solution ◽  
Severe Effect ◽  
Good Technique ◽  
Gravitational Search

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.

A Gravitational Search Algorithm Approach for Optimizing Closed-Loop Logistics Network

2012 ◽  
pp. 1768-1789
Author(s):  
Abdolhossein Sadrnia ◽  
Hossein Nezamabadi-Pour ◽  
Mehrdad Nikbakht ◽  
Napsiah Ismail
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Methods ◽  
Total Cost ◽  
Logistics Network ◽  
Network Problems ◽  
Gravitational Search ◽  
Law Of Gravity

Since late in the 20th century, various heuristic and metaheuristic optimization methods have been developed to obtain superior results and optimize models more efficiently. Some have been inspired by natural events and swarm behaviors. In this chapter, the authors illustrate empirical applications of the gravitational search algorithm (GSA) as a new optimization algorithm based on the law of gravity and mass interactions to optimize closed-loop logistics network. To achieve these aims, the need for a green supply chain will be discussed, and the related drivers and pressures motivate us to develop a mathematical model to optimize total cost in a closed-loop logistic for gathering automobile alternators at the end of their life cycle. Finally, optimizing total costs in a logistic network is solved using GSA in MATLAB software. To express GSA capabilities, a genetic algorithm (GA), as a common and standard metaheuristic algorithm, is compared. The obtained results confirm GSA’s performance and its ability to solve complicated network problems in closed-loop supply chain and logistics.

OPTIMAL TIMAH TASOH RESERVOIR IN, PERLIS: AN OPERATION USING THE GRAVITATIONAL SEARCH ALGORITHM (GSA)

2015 ◽  
Vol 77 (30) ◽  
Author(s):  
Asmadi Ahmad ◽  
Siti Fatin Mohd Razali ◽  
Ahmed El-Shafie ◽  
Zawawi Samba Mohamad
Keyword(s):  
Search Algorithm ◽  
Fitness Function ◽  
Gravitational Search Algorithm ◽  
Inertial Mass ◽  
Storage Condition ◽  
Sustainable Engineering ◽  
Release Policy ◽  
Gravitational Search ◽  
The Times ◽  
Law Of Gravity

The construction of a dam or a reservoir can have a serious impact the environment. When dealing with the increasing water demand from irrigation and water supply, alternative solution has to be sought way rather than building a new dam. Therefore, reservoir optimization can be employed as a new approach in sustainable engineering to solve this kind of problem. In this paper an optimization algorithm based on the Newton law of gravity, which is called Gravitational Search Algorithm (GSA), is introduced for optimal reservoir operation study. In GSA, every mass has four specifications, which are position, inertial mass, active gravitational mass, and passive gravitational.  The location of the mass is the solution of the problem, with the gravitational and inertial masses being determined by using a fitness function. Furthermore, The algorithm was applied to the Timah Tasoh reservoir and the release policy was tested by using simulation of demand and release. The result revealed that 72% of the times the reservoir managed to fulfill the demand to the users. Moreover, with the new optimized release policy, the dam operator can manage the reservoir release for the users by determining the inflow pattern, as well as by and observing the current storage condition as a guideline

Gravitational Search Algorithm and Its Variants

2016 ◽  
Vol 30 (08) ◽  
pp. 1639001 ◽  
Author(s):  
Nazmul Siddique ◽  
Hojjat Adeli
Keyword(s):  
Conceptual Framework ◽  
Global Solution ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Gravity Force ◽  
Newtonian Gravity ◽  
Global Movement ◽  
Gravitational Search ◽  
Laws Of Motion ◽  
Number Of Iterations

Gravitational search algorithm (GSA) is a nature-inspired conceptual framework with roots in gravitational kinematics, a branch of physics that models the motion of masses moving under the influence of gravity. In GSA, a collection of objects interacts with each other under the Newtonian gravity and the laws of motion. The performances of objects are measured by masses. All these objects attract each other by the gravity force, while this force causes a global movement of all objects toward the objects with heavier masses. The position of the object corresponds to a solution of the problem. The positions of the objects are updated every iteration and the best fitness along with its corresponding object is stored. Heavier masses move slowly than lighter ones. The algorithm terminates after a specified number of iterations after which the best fitness becomes the global fitness for a particular problem and the positions of the corresponding object becomes the global solution of that problem. This paper presents a review of GSA and its variants.

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

2014 ◽  
Vol 697 ◽  
pp. 450-455 ◽  
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.

A Gravitational Search Algorithm Approach for Optimizing Closed-Loop Logistics Network

2013 ◽  
pp. 616-638 ◽  
Author(s):  
Abdolhossein Sadrnia ◽  
Hossein Nezamabadi-Pour ◽  
Mehrdad Nikbakht ◽  
Napsiah Ismail
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Methods ◽  
Total Cost ◽  
Logistics Network ◽  
Network Problems ◽  
Gravitational Search ◽  
Law Of Gravity

Since late in the 20th century, various heuristic and metaheuristic optimization methods have been developed to obtain superior results and optimize models more efficiently. Some have been inspired by natural events and swarm behaviors. In this chapter, the authors illustrate empirical applications of the gravitational search algorithm (GSA) as a new optimization algorithm based on the law of gravity and mass interactions to optimize closed-loop logistics network. To achieve these aims, the need for a green supply chain will be discussed, and the related drivers and pressures motivate us to develop a mathematical model to optimize total cost in a closed-loop logistic for gathering automobile alternators at the end of their life cycle. Finally, optimizing total costs in a logistic network is solved using GSA in MATLAB software. To express GSA capabilities, a genetic algorithm (GA), as a common and standard metaheuristic algorithm, is compared. The obtained results confirm GSA’s performance and its ability to solve complicated network problems in closed-loop supply chain and logistics.

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