Discrete Gravitational Search Algorithm Research Articles

Disassembly sequence planning and application using simplified discrete gravitational search algorithm for equipment maintenance in hydropower station

Disassembly sequence planning (DSP) is a crucial way to optimize equipment maintenance process for hydropower equipment (HE). As a discrete combinatorial optimization problem with complex disassembly precedence constraints, however, the serious combination explosion caused by a large number of HE components will make the solution process difficult. Thus, a simplified discrete gravitational search algorithm (SDGSA) is proposed based on the core idea of the gravitational search algorithm (GSA). In the proposed algorithm, fast feasible solution generator (FFSG) is used to generate the initial population, precedence preservative operator (PPO) is utilized to generate the next generation efficiently, multipoint optimization operator (MOO) is applied to guide a solution to move to the better neighbor solution, and escaping operator (EO) is employed to prevent population falling into local optimum prematurely and improve the ability to find the optimal solution. In this study, the performance comparison experiments are carried out among SDGSA, simplified swarm optimization (SSO) algorithm, genetic algorithm (GA), simplified teaching-learning-based optimization (STLBO), and Team-Based Genetic Algorithm (TBGA). The results of the three maintenance tasks with different complexity in the flat plate water seal (FPWS) shows that in the solution of case 1-case 3, the proportion of the optimal sequence found by the SDGSA is 13.3 %, 70 %, and 70 % higher than comparison algorithms, respectively. The convergence speed and optimization ability are also better than other algorithms. Finally, the proposed method has been successfully applied to the automatic generation of virtual operation instruction (VOI) for equipment maintenance.

Multi-objective scheduling of priority-based rescue vehicles to extinguish forest fires using a multi-objective discrete gravitational search algorithm

The implementation of emergency scheduling of priority-based rescue vehicles for extinguishing forest fires is a complex optimization problem facing with many challenges and difficulties to optimize the operational costs and to improve the efficiency to make robust decisions. The main challenges are to minimize the number of fire engines while minimizing firefighting time and firefighting delay time, simultaneously. The main difficulties to make these decisions are to consider the severity of each fire point with regards to the limited resources of vehicles. Hence, this paper motivates to develop a new multi-objective scheduling model for extinguishing the fire of forests considering rescue priority with the limited rescue resources. To make an efficient and robust solution for the proposed problem, another novelty is to propose a hybrid optimization algorithm which is a modified discrete gravitational search algorithm. To confirm the applicability of the proposed problem, an actual forest fire emergency scheduling in Heilongjiang Province, China, is simulated. The proposed hybrid optimization algorithm is tested to check the feasibility the Pareto solutions and it is compared with a set of well-known and recent algorithms to show its efficiency. Finally, after a comprehensive discussion, the results prove that this work provides an accurate and effective tool for performing the emergency scheduling for extinguishing the forest fire.

Deep neural networks-based classification optimization by reducing the feature dimensionality with the variants of gravitational search algorithm

The optimization of the problems significantly improves the solution of the complex problems. The reduction in the feature dimensionality is enormously salient to reduce the redundant features and improve the system accuracy. In this paper, an amalgamation of different concepts is proposed to optimize the features and improve the system classification. The experiment is performed on the facial expression detection application by proposing the amalgamation of deep neural network models with the variants of the gravitational search algorithm. Facial expressions are the movement of the facial components such as lips, nose, eyes that are considered as the features to classify human emotions into different classes. The initial feature extraction is performed with the local binary pattern. The extracted feature set is optimized with the variants of gravitational search algorithm (GSA) as standard gravitational search algorithm (SGSA), binary gravitational search algorithm (BGSA) and fast discrete gravitational search algorithm (FDGSA). The deep neural network models of deep convolutional neural network (DCNN) and extended deep convolutional neural network (EDCNN) are employed for the classification of emotions from imagery datasets of JAFFE and KDEF. The fixed pose images of both the datasets are acquired and comparison based on average recognition accuracy is performed. The comparative analysis of the mentioned techniques and state-of-the-art techniques illustrates the superior recognition accuracy of the FDGSA with the EDCNN technique.

Optimal Deep Neural Network model based multimodality fused medical image classification

Medical image fusion and classification has been utilized to get valuable significant data multimodality medical image data. The fundamental commitment of this paper is to extricate the compelling features from the fused medical image that is Multimodality Medical images (MMI) for the classification model. At first, MMI are considered for the fusion procedure by shearlet Transform and optimization model to get fused images. The medical images of fusion has demonstrated to be valuable for propelling the clinical dependability of utilizing medical imaging for medical diagnostics and investigation, this procedure is considered as the principal phase. In send phase, few features are extracted from the Fused multimodality Medical Image (FMMI). For example, texture and XOR pattern models for classification, the point of recognition as given fused image as begin or malignant by Deep Neural Network (DNN) with Optimization. The significant purpose of optimization is to improve the classification accuracy. To optimize the DNN structure, we proposed the Discrete Gravitational Search Algorithm (DGSA), and it depends on increasing speed and force of DNN weights. When the optimal structure finds, the test image comprises the input of the structure for classification. The experimental results stated that the proposed model shows effective performance with the fusion factor of 6.52 and spatial frequency of 26.8.

A Modified Gravitational Search Algorithm for Discrete Optimization Problem

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.

A discrete gravitational search algorithm for the blocking flow shop problem with total flow time minimization

The blocking flow shop problem (BFSP) is one of the key models in the flow shop scheduling problem in the manufacturing systems. Gravitational Search Algorithm (GSA) is an algorithm based on the population for solving various optimization problems. However, GSA is scarcely applied to solve the BFSP as it is designed to solve the continuous problems. In this paper, a Discrete Gravitational Search Algorithm (DGSA) is presented for solving the BFSP with the total flow time minimization. A new variable profile fitting (VPF) combined with NEH heuristic, named VPF _ NEH(n), is introduced for balancing the quality and the diversity of the initial population to configure the DGSA. The three operators including the variable neighborhood operators (VNO), the path relinking and the plus operator are implemented during the location updating of the candidates. The objective of the operation is to prevent the premature convergence of the population and to balance the exploration and exploitation in the process of optimization. The expected runtime of the DGSA is analyzed by the level-based theorem. The simulated results indicate that the effectiveness and superiority of the DGSA.

MIMR-DGSA: Unsupervised hyperspectral band selection based on information theory and a modified discrete gravitational search algorithm

Band selection plays an important role in hyperspectral data analysis as it can improve the performance of data analysis without losing information about the constitution of the underlying data. We propose a MIMR-DGSA algorithm for band selection by following the Maximum-Information-Minimum-Redundancy (MIMR) criterion that maximises the information carried by individual features of a subset and minimises redundant information between them. Subsets are generated with a modified Discrete Gravitational Search Algorithm (DGSA) where we definine a neighbourhood concept for feature subsets. A fast algorithm for pairwise mutual information calculation that incorporates variable bandwidths of hyperspectral bands called VarBWFastMI is also developed. Classification results on three hyperspectral remote sensing datasets show that the proposed MIMR-DGSA performs similar to the original MIMR with Clonal Selection Algorithm (CSA) but is computationally more efficient and easier to handle as it has fewer parameters for tuning.

A discrete gravitational search algorithm for solving the cell formation problem

A discrete gravitational search algorithm for solving the cell formation problem

Probabilistic Performance-Based Optimum Seismic Design of RC Structures Considering Soil–Structure Interaction Effects

In spite of the advances in the development of efficient computational methods, reliability-based design optimization (RBDO) of large-scale structures subjected to seismic loading still requires prohibitive computational effort. Additional difficulties arise for the optimum design of reinforced concrete (RC) structures because they typically feature a wide variety of sections and shapes because of architectural requirements and versatility of construction. In this paper, a modified discrete gravitational search algorithm (MDGSA) is proposed and described, which is paired with a metamodel to minimize the total cost of structures (i.e., the sum of construction and repair costs) under deterministic and probabilistic constraints. MDGSA exploits the crossover and mutation operators used in the differential evolution optimization algorithm to improve the exploitation and exploration of the original gravitational search algorithm (GSA). The competency of the proposed method against the original GSA is demonstrated through performance-based optimum seismic design of an example nine-story RC building subject to both deterministic and probabilistic constraints. Nonlinear soil-structure interaction effects are taken into account in the dynamic finite element analysis of the soil-structure system. The metamodel, which is a weighted least squares support vector machine with the Morlet wavelet kernel function, is used to predict the seismic response of the structure. Annual probabilities of nonperformance are considered as the probabilistic constraints, and the Monte Carlo simulation technique is used to evaluate the seismic reliability of the structure. Results indicate that the proposed MDGSA in conjunction with the metamodel can provide an efficient and effective tool for the RBDO of large-scale structures.

DGSA: discrete gravitational search algorithm for solving knapsack problem

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.

Cognitive discrete gravitational search algorithm for solving 0-1 knapsack problem

Cognitive discrete gravitational search algorithm for solving 0-1 knapsack problem

Reliability-based design optimization of reinforced concrete structures including soil–structure interaction using a discrete gravitational search algorithm and a proposed metamodel

A new discrete gravitational search algorithm (DGSA) and a metamodelling framework are introduced for reliability-based design optimization (RBDO) of reinforced concrete structures. The RBDO of structures with soil–structure interaction (SSI) effects is investigated in accordance with performance-based design. The proposed DGSA is based on the standard gravitational search algorithm (GSA) to optimize the structural cost under deterministic and probabilistic constraints. The Monte-Carlo simulation (MCS) method is considered as the most reliable method for estimating the probabilities of reliability. In order to reduce the computational time of MCS, the proposed metamodelling framework is employed to predict the responses of the SSI system in the RBDO procedure. The metamodel consists of a weighted least squares support vector machine (WLS-SVM) and a wavelet kernel function, which is called WWLS-SVM. Numerical results demonstrate the efficiency and computational advantages of DGSA and the proposed metamodel for RBDO of reinforced concrete structures.

A discrete gravitational search algorithm for solving combinatorial optimization problems

Metaheuristics are general search strategies that, at the exploitation stage, intensively exploit areas of the solution space with high quality solutions and, at the exploration stage, move to unexplored areas of the solution space when necessary. The Gravitational Search Algorithm (GSA) is a stochastic population-based metaheuristic that was originally designed for solving continuous optimization problems. It has a flexible and well-balanced mechanism for enhancing exploration and exploitation abilities. In this paper, a Discrete Gravitational Search Algorithm (DGSA) is proposed to solve combinatorial optimization problems. The proposed DGSA uses a Path Re-linking (PR) strategy instead of the classic way in which the agents of GSA usually move from their current position to the position of other agents. The proposed algorithm was tested on a set of 54 Euclidean benchmark instances of TSP with sizes ranging from 51 to 2392 nodes. The results were satisfactory and in the majority of the instances, the results were equal to the best known solution. The proposed algorithm ranked ninth when compared with 54 different algorithms with regard to quality of the solution.

Facing the classification of binary problems with a GSA-SVM hybrid system

This paper hybridizes the gravitational search algorithm (GSA) with support vector machine (SVM) and makes a novel GSA-SVM hybrid system to improve classification accuracy with an appropriate feature subset in binary problems. In order to simultaneously optimize the input feature subset selection and the SVM parameter setting, a discrete GSA is combined with a continuous-valued GSA in this system. We evaluate the proposed hybrid system on several UCI machine learning benchmark examples. The results show that the proposed approach is able to select the discriminating input features correctly and achieve high classification accuracy which is comparable to or better than well-known similar classifier systems.