Workflow Scheduling in cloud computing has drawn enormous attention due to its wide application in both scientific and business areas. This is particularly an NP-complete problem. Therefore, many ...researchers have proposed a number of heuristics as well as meta-heuristic techniques by considering several issues, such as energy conservation, cost and makespan. However, it is still an open area of research as most of the heuristics or meta-heuristics may not fulfill certain optimum criterion and produce near optimal solution. In this paper, we propose a meta-heuristic based algorithm for workflow scheduling that considers minimization of makespan and cost. The proposed algorithm is a hybridization of the popular meta-heuristic, Gravitational Search Algorithm (GSA) and equally popular heuristic, Heterogeneous Earliest Finish Time (HEFT) to schedule workflow applications. We introduce a new factor called cost time equivalence to make the bi-objective optimization more realistic. We consider monetary cost ratio (MCR) and schedule length ratio (SLR) as the performance metrics to compare the performance of the proposed algorithm with existing algorithms. With rigorous experiments over different scientific workflows, we show the effectiveness of the proposed algorithm over standard GSA, Hybrid Genetic Algorithm (HGA) and the HEFT. We validate the results by well-known statistical test, Analysis of Variance (ANOVA). In all the cases, simulation results show that the proposed approach outperforms these algorithms.
•Proposed an efficient hybrid scheme of GSA and HEFT, called HGSA for workflow scheduling.•Systematic derivation of fitness function based on makespan and cost.•Novelty in introducing a proficient elimination strategy of inferior agents.•Demonstration of better performance through simulation results and statistical test ANOVA.
•Disruption operator based gravitational search algorithm is proposed.•The proposed method is applied to solve short term hydrothermal scheduling.•Proper tuning of parameters is done to achieve ...optimal results.•The effectiveness of the proposed approach has been verified on two test systems.•Results obtained are validated with other recent reported methods in literature.
This paper proposes a disruption based gravitational search algorithm (DGSA) to find the optimum solution for short term hydrothermal scheduling (STHTS) problem, which considers the cascading nature of hydro plants, water transportation time delays between reservoirs, variable hourly water discharge limits, reservoir storage volume limits, hydraulic continuity constraint, initial and final reservoir storage volume limits, power system load balance, generation limits of hydro and thermal units and valve point loading effects of thermal plants. In the proposed approach, a disruption operator based on astrophysics has been integrated into gravitational search algorithm (GSA) to enhance its performance. It is found that the disruption operator increases both of the exploration and exploitation abilities in comparison with the conventional GSA. Moreover, an effective strategy is utilized to handle the end storage volume constraints and system load balance constraints. Finally, the proposed approach is evaluated on two hydrothermal test systems, one consisting of four hydro plants and an equivalent thermal plant and another with four hydro and three thermal plants. The comparative result analysis shows that the DGSA approach has better solution results and convergence accuracy than the GSA and other approaches reported in literature for solving STHTS problem.
After more than ten years of exponential development, the growth rate of cruise tourist in China is slowing down. There is increasingly financial risk of investing in homeports, cruise ships and ...promotional activities. Therefore, forecasting Chinese cruise tourism demand is a prerequisite for investment decision-making and planning. In order to enhance forecasting performance, a least squares support vector regression model with gravitational search algorithm (LSSVR-GSA) is proposed for forecasting cruise tourism demand with big data, which are search query data (SQD) from Baidu and economic indexes. In the proposed model, hyper-parameters of the LSSVR model are optimized with GSA. By comparing these models with various settings, we find that LSSVR-GSA with selected mobile keywords and economic indexes can achieve the highest forecasting performance. The results indicate the proposed framework of the methodology is effective and big data can be helpful predictors for forecasting Chinese cruise tourism demand.
Gravitational search algorithm is an effective population-based algorithm. It simulates the law of gravity to implement the interaction among particles. Although it can effectively optimize many ...problems, it generally suffers from premature convergence and low search capability. To address these limitations, a gravitational search algorithm with hierarchy and distributed framework is proposed. A distributed framework randomly groups several subpopulations and a three-layered hierarchy manages them. Communication among subpopulations finally enhances the search performance. Experiments discuss parameters and strategies of the proposed algorithm. Comparison between it and sixteen state-of-the-art algorithms demonstrates its superior performance. It also shows the practicality for two real-world optimization problems.
•A new hierarchical and distributed gravitational search algorithm is proposed.•Its hierarchical and distributed structures exert different search effects.•Its performance is significantly enhanced in comparison with other algorithms.•It shows the practicality for real-world optimization problems.•Its computational efficiency is improved.
In recent years, the integration of renewable generation into micro-grid has been growing. Therefore, it is essential to optimize the power generation from multiple sources with minimal cost. This ...paper presents a Memory-Based Gravitational Search Algorithm (MBGSA) for solving the economic load dispatch in a micro-grid. The problem with current metaheuristic optimization techniques and the conventional gravitational search algorithm (GSA) are largely associated with slow gathering rate, less memory to save the best agent position of the optimal solution and poor performance in solving the complex optimization problems. The MBGSA is based on the concept of saving the best solution of the agent from the last iteration to calculate the new agent based on Newton's laws of gravitation. In this work, the MBGSA has been utilized to optimize power generation from multiple generation sources such as Photovoltaic (PV) systems, combined heat power (CHP) systems, and diesel generators. The results have been compared to classic methods such as Quadratic Programming (QP) and other metaheuristics techniques such as the GSA, Artificial Bee Colony (ABC), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results illustrate that the proposed method has higher performance in solving the optimal power generation problem compared to other methods.
This paper presents the automatic load frequency control (ALFC) of two-area multisource hybrid power system (HPS). The interconnected HPS model consists of conventional and renewable energy sources ...operating in disparate combinations to balance the generation and load demand of the system. In the proffered work, the stability analysis of nonlinear dynamic HPS model was analyzed using the Hankel method of model order reduction. Also, an attempt was made to apply cascade proportional integral - proportional derivative (PI-PD) control for HPS. The gains of the controller were optimized by minimizing the integral absolute error (IAE) of area control error using particle swarm optimization-gravitational search algorithm (PSO-GSA) optimization technique. The performance of cascade control was compared with other classical controllers and the efficiency of this approach was studied for various cases of HPS model. The result shows that the cascade control produced better transient and steady state performances than those of the other classical controllers. The robustness analysis also reveals that the system overshoots/undershoots in frequency response pertaining to random change in wind power generation and load perturbations were significantly reduced by the proposed cascade control. In addition, the sensitivity analysis of the system was performed, with the variation in step load perturbation (SLP) of 1% to 5%, system loading and inertia of the system by ±25% of nominal values to prove the efficiency of the controller. Furthermore, to prove the efficiency of PSO-GSA tuned cascade control, the results were compared with other artificial intelligence (AI) methods presented in the literature. Further, the stability of the system was analyzed in frequency domain for different operating cases.
Monthly streamflow prediction plays a significant role in reservoir operation and water resource management. Hence, this research tries to develop a hybrid model for accurate monthly streamflow ...prediction, where the ensemble empirical mode decomposition (EEMD) is firstly used to decompose the original streamflow data into a finite amount of intrinsic mode functions (IMFs) and a residue; and then the extreme learning machine (ELM) is employed to forecast each IMFs and the residue, while an improved gravitational search algorithm (IGSA) based on elitist-guide evolution strategies, selection operator and mutation operator is used to select the parameters of all the ELM models; finally, the summarized predicated results for all the subcomponents are treated as the final forecasting result. The hybrid method is applied to forecast the monthly runoff of Three Gorges in China, while four quantitative indexes are used to test the performances of the developed forecasting models. The results show that EEMD can effectively separate the internal characteristics of the original monthly runoff, and the hybrid model is able to make an obvious improvement over other models in hydrological time series prediction.
•EEMD is used to decompose streamflow into several IMFs and a residue.•ELM models are constructed to forecast all the extracted subcomponents.•IGSA is used to find satisfying parameter combination for each ELM model.•The hybrid method provides better forecasting results than other methods.
Disassembly is indispensable to recycle and remanufacture end-of-life products, and a disassembly line-balancing problem (DLBP) is studied frequently. Recent research on disassembly lines has focused ...on a complete disassembly for optimising the balancing ability of lines. However, a partial disassembly process is widely applied in the current industry practice, which aims at reusing valuable components and maximising the profit (or minimising the cost). In this paper, we consider a profit-oriented partial disassembly line-balancing problem (PPDLBP), and a mathematical model of this problem is established, which is to achieve the maximisation of profit for dismantling a product in DLBP. The PPDLBP is NP-complete since DLBP is proven to be a NP-complete problem, which is usually handled by a metaheuristics. Therefore, a novel efficient approach based on gravitational search algorithm (GSA) is proposed to solve the PPDLBP. GSA is an optimisation technique that is inspired by the Newtonian gravity and the laws of motion. Also, two different scale cases are used to test on the proposed algorithm, and some comparisons with the CPLEX method, particle swarm optimisation, differential evolution and artificial bee colony algorithms are presented to demonstrate the excellence of the proposed approach.
Multi-level image thresholding segmentation divides an image into multiple non-overlapping regions. This paper presents a novel two-dimensional (2D) histogram-based segmentation method to improve the ...efficiency of multi-level image thresholding segmentation. In the proposed method, a new non-local means 2D histogram and a novel variant of gravitational search algorithm (exponential Kbest gravitational search algorithm) have been used to find the optimal thresholds. Further, for the optimization, a 2D Rényi entropy has been redefined for multi-level thresholding. The proposed method has been tested on the Berkeley Segmentation Dataset and Benchmark (BSDS300) in terms of both subjective and objective assessments. The experimental results affirm that the proposed method outperforms the other 2D histogram-based image thresholding segmentation methods on majority of performance parameters.
•A 2D non-local means exponential Kbest GSA for image segmentation is proposed.•A novel 2D histogram is presented based on non-local means.•An exponential Kbest GSA is introduced to find optimal thresholds in 2D histogram.•The Renyi entropy is redefined for multilevel thresholding on 2D histogram.•The proposed method outperforms existing methods on BSDS300 dataset.
•A new aggregative learning gravitational search algorithm is proposed.•A self-adaptive gravitational constant is introduced into the algorithm.•Extensive performance comparison with other ...state-of-the-art algorithms is done.•Neural network learning tasks show the proposed algorithm’s practicability.•The time complexity of the proposed algorithm is analyzed.
The gravitational search algorithm (GSA) is a meta-heuristic algorithm based on the theory of Newtonian gravity. This algorithm uses the gravitational forces among individuals to move their positions in order to find a solution to optimization problems. Many studies indicate that the GSA is an effective algorithm, but in some cases, it still suffers from low search performance and premature convergence. To alleviate these issues of the GSA, an aggregative learning GSA called the ALGSA is proposed with a self-adaptive gravitational constant in which each individual possesses its own gravitational constant to improve the search performance. The proposed algorithm is compared with some existing variants of the GSA on the IEEE CEC2017 benchmark test functions to validate its search performance. Moreover, the ALGSA is also tested on neural network optimization to further verify its effectiveness. Finally, the time complexity of the ALGSA is analyzed to clarify its search performance.
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