A Modified Gravitational Search Algorithm for Discrete Optimization Problem
28 Feb 2020-International journal of simulation: systems, science and technology (United Kingdom Simulation Society)-Vol. 15, Iss: 1, pp 51-55
TL;DR: The experimental result shows that the FDGSA able to find better solutions and converges faster compared to the Binary Gravitational Search Algorithm.
Abstract: This paper presents a modified Gravitational Search Algorithm (GSA) called Discrete Gravitational Search Algorithm (DGSA) for discrete optimization problems. In DGSA, an agent’s position is updated based on its direction and velocity. Both the direction and velocity determine the candidates of integer values for the position update of an agent and then the selection is done randomly. Unimodal test functions are used to evaluate the performance of the proposed DGSA. The experimental result shows that the FDGSA able to find better solutions and converges faster compared to the Binary Gravitational Search Algorithm.
Citations
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TL;DR: A Discrete version of the GSA (DGSA) for solving 0–1 knapsack problem is introduced and the effectiveness of the DGSA in comparison with other similar algorithms in terms of the accuracy and overcoming the defect of local convergence is shown.
Abstract: The 0–1 knapsack problem is one of the classic NP-hard problems. It is an open issue in discrete optimization problems, which plays an important role in the real applications. Therefore, several algorithms have been developed to solve it. The Gravitational Search Algorithm (GSA) is an optimization algorithm based on the law of gravity and mass interactions. In the GSA, the searcher agents are a collection of masses that interact with each other based on the Newtonian gravity and the laws of motion. In this algorithm the position of the agents can be considered as the solutions. The GSA is a nature-inspired algorithm that is used for finding the optimum value of continuous functions. This paper introduces a Discrete version of the GSA (DGSA) for solving 0–1 knapsack problem. In this regard, we introduce an approach for discretely updating the position of each agent. In addition, a fitness function has been proposed for 0–1 knapsack problem. Our experimental results show the effectiveness of the DGSA in comparison with other similar algorithms in terms of the accuracy and overcoming the defect of local convergence.
15 citations
TL;DR: A Cognitive Discrete GSA (called CDGSA) for solving 0-1 knapsack problem based on attracting each particle with two cognitive and social components, which has gained the better accuracy in comparison of the other similar methods.
Abstract: The Gravitational Search Algorithm (GSA) has been proposed for solving continues problems based on the law of gravity. In this paper, we propose a Cognitive Discrete GSA (called CDGSA) for solving 0-1 knapsack problem. The GSA has used a function of time to determine the number of the best particles for attracting others in each time, while our main idea is based on attracting each particle with two cognitive and social components. The cognitive component contains the best position of the particles up to now, while the social component contains the particle with the best position in the whole of the system at the current time and the particles with the best position in the neighborhood. In the other words, the cognitive component is such an appropriate actuator for embedding in the intelligent agents like robots. Such intelligent agent or robot is guided in the right direction with the help of its best previous position. Finally, by introducing discrete version of this idea, the efficiency of the proposed algorithm is measured for 0-1 knapsack problem. Experimental results on some benchmark and high dimensional problems illustrate that the proposed algorithm has gained the better accuracy in comparison of the other similar methods.
9 citations
TL;DR: In this paper, various optimization techniques have been studied for non-linear state estimation based on EKF.
Abstract: estimation is the common problem in every area of engineering. There are different filters used to overcome the problem of state estimation like Kalman filter, Particle filters etc. Kalman Filter is popular when the system is linear but when the system is highly non-linear then the different derivatives of Kalman Filter are used like Extended Kalman Filter (EKF), Unscented Kalman filter. But these estimation techniques require tuning of process and noise covariance matrices. The different optimization techniques are used to tune the filter parameters of EKF. In this paper, various optimization techniques have been studied for non-linear state estimation based on EKF.
7 citations
Cites background or methods from "A Modified Gravitational Search Alg..."
...[15] Fast Discrete GSA To solve discrete optimization problems Convergence, mean, median, S....
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...Another variant of Gravitational Search Algorithm called Fast Discrete Gravitational Search Algorithm (FDGSA) has been proposed in [15] to solve discrete optimization problems using integer values....
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01 Apr 2016
TL;DR: A new velocity update strategy is proposed, in which new velocity depends on the previous velocity and the acceleration, based on the fitness of the solutions, which shows that FBGSA is a competitive variant of GSA algorithm.
Abstract: Gravitational search algorithm (GSA) is a swarm intelligence based optimization algorithm which is based on the law of gravity and the law of motion of mass interaction between individuals. In GSA, the solution search process depends on the velocity which is a function of acceleration and the previous velocity. In the solution search process, acceleration plays important role and depends on the masses and forces of the individuals. Due to this component, GSA some times slow in convergence, while some time prematurely converge to the local optima. To avoid this situation, a new velocity update strategy is proposed, in which new velocity depends on the previous velocity and the acceleration, based on the fitness of the solutions. The proposed strategy is named as fitness based gravitational search algorithm (FBGSA). In FBGSA, the high fit solutions are motivated to exploit the promising search regions, while the low fit solutions have to explore the search space. Further, performance of the proposed strategy is compared with basic GSA and another swarm intelligence based algorithm, namely biogeography based optimization (BBO) algorithm over 16 different benchmark functions. Reported results show that FBGSA is a competitive variant of GSA algorithm.
6 citations
TL;DR: Gravitational search algorithm and differential evolution is applied to optimize the inverse calculation neural network mapping model of demolition robot, and the algorithm simulation shows that gravity can effectively regulate the convergence process of differential evolution population.
Abstract: For the inverse calculation of laser-guided demolition robot, its global nonlinear mapping model from laser measuring point to joint cylinder stroke has been set up with an artificial neural networ...
5 citations
Cites methods from "A Modified Gravitational Search Alg..."
...As a meta-heuristic algorithm, GSA uses gravity to transfer information between different particles and guides individuals move by properly adjusting gravity and inertia mass.(36,37) Evolutionary algorithm (including DE) has a good global search capability due to its randomness, but it is difficult to find a balance between exploration and exploitation, so the convergence accuracy is low....
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References
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06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
35,104 citations
"A Modified Gravitational Search Alg..." refers methods in this paper
...Objects with heavier mass have stronger attraction and move slower than the objects with relatively smaller mass. Results in [1] showed that GSA performed considerably better compared to Particle Swarm Optimization (PSO) [2] and Central Force Optimization (CFO) [3]....
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...Results in [1] showed that GSA performed considerably better compared to Particle Swarm Optimization (PSO) [2] and Central Force Optimization (CFO) [3]....
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TL;DR: A new optimization algorithm based on the law of gravity and mass interactions is introduced and the obtained results confirm the high performance of the proposed method in solving various nonlinear functions.
5,501 citations
"A Modified Gravitational Search Alg..." refers methods in this paper
...Results in [1] showed that GSA performed considerably better compared to Particle Swarm Optimization (PSO) [2] and Central Force Optimization (CFO) [3]....
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TL;DR: A binary version of the gravitational search algorithm, based on the law of gravity and mass interactions, is introduced and the experimental results confirm the efficiency of the BGSA in solving various nonlinear benchmark functions.
Abstract: Gravitational search algorithm is one of the new optimization algorithms that is based on the law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. In this article, a binary version of the algorithm is introduced. To evaluate the performances of the proposed algorithm, several experiments are performed. The experimental results confirm the efficiency of the BGSA in solving various nonlinear benchmark functions.
702 citations
"A Modified Gravitational Search Alg..." refers methods in this paper
...EXPERIMENT, RESULT, AND DISCUSSION Five benchmark functions used in this study, which are taken from [4], are shown in Table 1....
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...GSA for Discrete Search Problem To solve discrete optimization using Binary Gravitational Search Algorithm (BGSA) [4], the position update is modified to accommodate with binary search space....
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01 Jan 2008
TL;DR: This paper presents Central Force Optimization, a novel, nature inspired, deterministic search metaheuristic for constrained multi-dimensional optimization based on the metaphor of gravitational kinematics, which exhibits very good performance.
Abstract: This paper presents Central Force Optimization, a novel, nature inspired, deterministic search metaheuristic for constrained multi-dimensional optimization. CFO is based on the metaphor of gravitational kinematics. Equations are presented for the positions and accelerations experienced by “probes” that “fly” through the decision space by analogy to masses moving under the influence of gravity. In the physical universe, probe satellites become trapped in close orbits around highly gravitating masses. In the CFO analogy, “mass” corresponds to a user-defined function of the value of an objective function to be maximized. CFO is a simple algorithm that is easily implemented in a compact computer program. A typical CFO implementation is applied to several test functions. CFO exhibits very good performance, suggesting that it merits further study.
123 citations
"A Modified Gravitational Search Alg..." refers methods in this paper
...Objects with heavier mass have stronger attraction and move slower than the objects with relatively smaller mass. Results in [1] showed that GSA performed considerably better compared to Particle Swarm Optimization (PSO) [2] and Central Force Optimization (CFO) [3]....
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...Results in [1] showed that GSA performed considerably better compared to Particle Swarm Optimization (PSO) [2] and Central Force Optimization (CFO) [3]....
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