DGSA: discrete gravitational search algorithm for solving knapsack problem

Sajedi, Hedieh; Razavi, Seyedeh Fatemeh

doi:10.1007/s12351-016-0240-2

Cited by 8 publications

(4 citation statements)

References 54 publications

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“…Similarly, a discrete version of GSA was developed to solve the 0-1 knapsack problem by modifying the position equation and fitness function of standard GSA. The simulation outcomes indicated better performance of discrete GSA in terms of faster convergence rate and better accuracy (Sajedi et al, 2017).…”

Section: Literature Surveymentioning

confidence: 94%

Levy Flight and Chaos Theory-Based Gravitational Search Algorithm for Global Optimization

Rather

Bala

2022

International Journal of Applied Metaheuristic Computing

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The Gravitational Search Algorithm (GSA) is one of the highly regarded population-based algorithms. It has been reported that GSA has a powerful global exploration capability but suffers from the limitations of getting stuck in local optima and slow convergence speed. In order to resolve the aforementioned issues, a modified version of GSA has been proposed based on levy flight distribution and chaotic maps (LCGSA). In LCGSA, the diversification is performed by utilizing the high step size value of levy flight distribution while exploitation is carried out by chaotic maps. The LCGSA is tested on well-known 23 classical benchmark functions. Moreover, it is also applied to three constrained engineering design problems. Furthermore, the analysis of results is performed through various performance metrics like statistical measures, convergence rate, and so on. Also, a signed Wilcoxon rank-sum test has also been conducted. The simulation results indicate that LCGSA provides better results as compared to standard GSA and most of the competing algorithms.

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Section: Literature Surveymentioning

confidence: 94%

Levy Flight and Chaos Theory-Based Gravitational Search Algorithm for Global Optimization

Rather

Bala

2022

International Journal of Applied Metaheuristic Computing

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“…These agents are generated randomly, but their feasibility conditions are investigated. Better solutions are related to the heaviest agents and at each iteration, the heaviest agent is selected as the best solution and other agents are attracted by this solution 33 . The mass of agent i in the iteration t is calculated by

\begin{align} m_{i}^{t} = \frac{f_{i}^{t} prefix- {w o r s t}^{t}}{{b e s t}^{t} prefix- {w o r s t}^{t}}, \end{align}

where worst t and best t present the worst and the best objective function values of iteration t , respectively.…”

Section: Solution Methodologymentioning

confidence: 99%

Mathematical formulation and hybrid meta‐heuristic algorithms for multiproduct oil pipeline scheduling problem with tardiness penalties

Goudarzi

Maleki

Niroomand

2021

Concurrency and Computation

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Summary The system under investigation contains a single refinery, a unique distribution center, and a multiproduct pipeline. The basic aim is to plan the optimal sequence for pumping products to achieve financial benefit and satisfy the customers with on‐time delivery. In this study, some restrictions (such as batch sizing, discharging rate, forbidden sequences, and settling periods) are considered and the problem is formulated as a MILP model. Although the multiproduct pipeline scheduling problem has high time complexity, meta‐heuristic algorithms have been used rarely in the literature. Another contribution of this work is to develop several meta‐heuristic algorithms to solve the proposed MILP effectively. Therefore, as a novelty, some classical meta‐heuristics like population‐based simulated annealing and population‐based variable neighborhood search are hybridized by the gravitational search algorithm for obtaining better performance. Parameters of the algorithms are tuned by an optimization problem and then all algorithms are compared by numerical examples. The achieved results demonstrate the validity of the model and the efficient performance of the proposed algorithms against exact methods. These algorithms also lead to better solutions in much lower computational time. Among them, the hybrid algorithm obtained by combining the SA and GSA meta‐heuristics are superior to the other algorithms.

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“…Continuous type with real-valued variables can resolve real problems [19], [45], dynamic constrained problem [46], multi-modal and multi-objective problems [24], [26]. Discrete type with discrete values can address binary problem [47], graph planarization problem [48] and knapsack problem [49]. Mixed type can tackle problems with both continuous and discrete variables [50], [51].…”

Section: Introductionmentioning

confidence: 99%

A Gravitational Search Algorithm With Chaotic Neural Oscillators

Wang

Gao

et al. 2020

IEEE Access

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Gravitational search algorithm (GSA) inspired from physics emulates gravitational forces to guide particles' search. It has been successfully applied to diverse optimization problems. However, its search performance is limited by its inherent mechanism where gravitational constant plays an important role in gravitational forces among particles. To improve it, this paper uses chaotic neural oscillators to adjust its gravitational constant, named GSA-CNO. Chaotic neural oscillators can generate various chaotic states according to their parameter settings. Thus, we select four kinds of chaotic neural oscillators to form distinctive chaotic characteristics. Experimental results show that chaotic neural oscillators effectively tune the gravitational constant such that GSA-CNO has good performance and stability against four GSA variants on functions. Three real-world optimization problems demonstrate the promising practicality of GSA-CNO.

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DGSA: discrete gravitational search algorithm for solving knapsack problem

Cited by 8 publications

References 54 publications

Levy Flight and Chaos Theory-Based Gravitational Search Algorithm for Global Optimization

Levy Flight and Chaos Theory-Based Gravitational Search Algorithm for Global Optimization

Mathematical formulation and hybrid meta‐heuristic algorithms for multiproduct oil pipeline scheduling problem with tardiness penalties

A Gravitational Search Algorithm With Chaotic Neural Oscillators

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