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Neural network and fuzzy system for the tuning of gravitational search algorithm parameters

EXPERT SYSTEMS WITH APPLICATIONS 2018年 102卷 234-244页

作者： Pelusi, Danilo Mascella, Raffaele Tallini, Luca Nayak, Janmenjoy Naik, Bighnaraj Abraham, Ajith Univ Teramo Fac Commun Sci Teramo Italy Sri Sivani Coll Engn Dept Comp Sc Engn Srikakulam India Veer Surendra Sai Univ Technol Dept Comp Applicat Burla Odisha India Machine Intelligence Res Labs MIR Labs Sci Networ Washington DC USA

A good trade-off between exploration and exploitation to find optimal values in search algorithms is very hard to achieve. On the other hand, the combination of search methods may cause computational complexity increase problems. The gravitational search algorithm (GSA) is a swarm optimization algorithm based on the law of gravity, where the solution search process depends on the velocity of particles. The application of intelligent techniques can improve the search performances of GSA. This paper proposes the design of a Neuro and Fuzzy gravitational search algorithm (NFGSA) to achieve better results than GSA in terms of global optimum search capability and convergence speed, without increasing the computational complexity. Both the algorithms have the same computational complexity O(nd), where n is the number of agents and d is the search space dimension. The main task of the designed intelligent system is to adjust a GSA parameter on a revised version of GSA. NFGSA is compared with GSA, a Plane Surface gravitational search algorithm (PSGSA) and a Modified gravitational search algorithm (MGSA). The results show that NFGSA improves the optimization performances of GSA and PSGSA, without adding computational costs. Moreover, the proposed algorithm is better than MGSA for a benchmark function and achieves similar results for two test functions. The analysis on the computational complexity shows that NFGSA has a better computational complexity than MGSA, because NFGSA has complexity O(nd), whereas MGSA has complexity O((nd)(2)). (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Computational intelligence Fuzzy systems gravitational search algorithm Neural networks search algorithms

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A comparative evaluation of gravitational search algorithm (GSA) against Artificial Bee Colony (ABC) for thermodynamic performance a geothermal power plant

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ENERGY 2018年第PartA期165卷 1061-1077页

作者： Ozkaraca, Osman Mugla Sitki Kocman Univ Technol Fac Dept Informat Syst Engn TR-48000 Mugla Turkey

Optimizing a complex system/problem under real working conditions with optimization methods means ensuring that they operate more efficiently, economical, and eco-friendly. For this purpose, in order to maximize the exergy efficiency of a thermodynamic model of a real operated geothermal power plant (GPP), two optimization methods, namely gravitational search algorithm (GSA) and Artificial Bee Colony (ABC), have been comparatively evaluated in this study. The selected thermodynamic model is a problem that is highly complex, non-linear and unsolvable through mathematical methods. In order to solve the problem, 17 optimization parameters have been selected on the model. In addition, the selected parameters have been divided into 11 groups according to the system equipment specifications to reduce time loss. The results of the study reported that GSA and ABC maximized the exergy efficiency of the real system from 14.52% to 26.31% and 23.92% respectively. The effects of the optimized parameters on the model are observed, and it has been verified by GPP operators, engineers and researchers that no contrariety to logic and engineering discipline existed. Hence, the results of GSA method for the engineering problem addressed in this study are better than those of ABC method and they responded in a much shorter time. The most effective group in both methods is the G3 group related to the turbines. Besides, the most effective optimization parameters on the system performance are the pressure differences in evaporators and mass flow of the geothermal fluid. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Optimization gravitational search algorithm Artificial Bee Colony Geothermal power plant Exergy efficiency Performance comparison

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Cost optimized Hybrid Genetic-gravitational search algorithm for load scheduling in Cloud Computing

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APPLIED SOFT COMPUTING 2019年 83卷 105627-000页

作者： Chaudhary, Divya Kumar, Bijendra Netaji Subhas Inst Technol Dept Comp Engn New Delhi India

In cloud computing, cost optimization is a prime concern for load scheduling. The swarm based meta-heuristics are prominently used for load scheduling in distributed computing environment. The conventional load scheduling approaches require a lot of resources and strategies which are non-adaptive and static in the computation, thereby increasing the response time, waiting time and the total cost of computation. The swarm intelligence-based load scheduling is adaptive, intelligent, collective, random, decentralized, self-collective, stochastic and is based on biologically inspired mechanisms than the other conventional mechanisms. The genetic algorithm schedules the particles based on mutation and crossover techniques. The force and acceleration acting on the particle helps in the finding the velocity and position of the next particle. The best position of the particles is assigned to cloudlets to be executed on the virtual machines in the cloud. The paper proposes a new load scheduling technique, Hybrid Genetic-gravitational search algorithm (HG-GSA) for reducing the total cost of computation. The total computational cost includes cost of execution and transfer. It works on hybrid crossover technique based gravitational search algorithm for searching the best position of the particle in the search space. The best position of the particle is used calculating the force. The HG-GSA is compared to the existing approaches in the CloudSim simulator. By the convergence and statistical analysis of the results, the proposed HG-GSA approach reduces the total cost of computation considerably as compared to existing PSO, Cloudy-GSA and LIGSA-C approaches. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Cloud computing Load scheduling gravitational search algorithm Swarm intelligence Genetic algorithm

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Modifying the gravitational search algorithm: A functional study

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INFORMATION SCIENCES 2018年 430卷 87-103页

作者： Minarova, M. Paternain, D. Jurio, A. Ruiz-Aranguren, J. Takac, Z. Bustince, H. Slovak Univ Technol Bratislava Radlinskeho 911 Bratislava 81005 Slovakia Univ Publ Navarra Campus Arrosadia S-N Pamplona 31006 Spain Inst Smart Cities Campus Arrosadia S-N Pamplona 31006 Spain

In this paper we replace the product of the masses in the gravitational search algorithm introduced by [17] by other bivariate functions with specific properties. We analyze the properties of these functions to guarantee convergence in the algorithm and we discuss an application to justify our theoretical study and the need of using functions other than the product. (C) 2017 Elsevier Inc. All rights reserved.

关键词： gravitational search algorithm Aggregation function t-norm, overlap function

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Accelerated multi-gravitational search algorithm for size optimization of truss structures

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 38卷 109-119页

作者： Khatibinia, Mohsen Yazdani, Hessam Univ Birjand Dept Civil Engn Birjand Iran Howard Univ Dept Civil & Environm Engn Washington DC 20059 USA

Weak local exploitation capability of the gravitational search algorithm (GSA) and its slow convergence rate in final iterations have been demonstrated in the literature. This paper presents a modified GSA denoted here as the accelerated multi-gravitational search algorithm (AMGSA) that exhibits an improved convergence rate. In AMGSA, the simplex crossover (SPX) and the operator mutation of the breeder genetic algorithm (BGA) are incorporated with the multi-gravitational search algorithm (MGSA) to achieve an algorithm with a good exploration-exploitation balance. MGSA is adopted to prevent stagnation of the search into a local optimum (i.e. to improve the exploration capability), while the SPX and the BGA mutation operator are used to bias the search toward promising areas of the search space (i.e. to promote local exploitation). The performance of AMGSA is evaluated using several benchmark truss optimization examples. Results indicate that AMGSA not only exhibits an improved balance between the exploration and exploitation schemes but also shows competitive promise in effectively and efficiently solving large-scale optimization problems as it requires a significantly lower number of structural analyses compared to other algorithms that it is checked against.

关键词： Size optimization Truss structures gravitational search algorithm Simplex crossover Breeder genetic algorithm

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A stability constrained adaptive alpha for gravitational search algorithm

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KNOWLEDGE-BASED SYSTEMS 2018年 139卷 200-213页

作者： Sun, Genyun Ma, Ping Ren, Jinchang Zhang, Aizhu Jia, Xiuping China Univ Petr East China Sch Geosci Qingdao 266580 Peoples R China Qingdao Natl Lab Marine Sci & Technol Lab Marine Mineral Resources Qingdao 266071 Peoples R China Univ New South Wales Canberra Sch Engn & Informat Technol Canberra ACT 2600 Australia Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland

gravitational search algorithm (GSA), a recent meta-heuristic algorithm inspired by Newton's law of gravity and mass interactions, shows good performance in various optimization problems. In GSA, the gravitational constant attenuation factor alpha (alpha) plays a vital role in convergence and the balance between exploration and exploitation. However, in GSA and most of its variants, all agents share the same a value without considering their evolutionary states, which has inevitably caused the premature convergence and imbalance of exploration and exploitation. In order to alleviate these drawbacks, in this paper, we propose a new variant of GSA, namely stability constrained adaptive alpha for GSA (SCAA). In SCAA, each agent's evolutionary state is estimated, which is then combined with the variation of the agent's position and fitness feedback to adaptively adjust the value of a. Moreover, to preserve agents' stable trajectories and improve convergence precision, a boundary constraint is derived from the stability conditions of GSA to restrict the value of alpha in each iteration. The performance of SCAA has been evaluated by comparing with the original GSA and four alpha adjusting algorithms on 13 conventional functions and 15 complex CEC2015 functions. The experimental results have demonstrated that SCAA has significantly better searching performance than its peers do. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Meta-heuristic algorithm gravitational search algorithm Adaptive parameter Stability conditions Exploration and exploitation

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An effective gbest-guided gravitational search algorithm for real-parameter optimization and its application in training of feedforward neural networks

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KNOWLEDGE-BASED SYSTEMS 2018年 143卷 192-207页

作者： Bohat, Vijay Kumar Arya, K. V. ABV Indian Inst Informat Technol & Management Multimedia & Informat Secur Res Grp Gwalior 474015 India

gravitational search algorithm (GSA) is a recently introduced meta-heuristic that has shown great performance in numerical function optimization and solving real world problems. GSA provides an excellent social interaction between its search agents. This social interaction results in admirable exploration of the search space and gives a unique social component to GSA. However, the social interaction is not able to exploit good solutions in an efficient manner. To overcome this problem, a novel algorithm named as gbest-guided gravitational search algorithm (GG-GSA) has been proposed by utilizing the global best (gbest) solution in the force calculation equation of GSA. The employment of gbest solution in any optimization algorithm is a tough task and can lead to premature convergence. In the proposed algorithm, the gbest solution is used adaptively and is able to achieve a better trade-off between exploration and exploitation. The performance of the proposed algorithm is compared with GSA and its variants on different suites of well-known benchmark test functions. The experimental results show that the GG-GSA performs better than other algorithms for most of the benchmark test functions. Furthermore, to test the ability of the proposed algorithm in solving real world applications, training of feedforward neural network problem is chosen. The results demonstrated the exceptional performance of GG-GSA on real world data-set. (C) 2017 Elsevier B.V. All rights reserved.

关键词： gravitational search algorithm GSA CEC 2014 Social component gbest Training of feedforward neural networks

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Optimising the thermal environment and the ampacity of underground power cables using the gravitational search algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第2期12卷 423-430页

作者： Perovic, Bojan D. Tasic, Dragan S. Klimenta, Dardan O. Radosavljevic, Jordan N. Jevtic, Miroljub D. Milovanovic, Milos. J. Univ Pristina Kosovska Mitrovica Fac Tech Sci Kneza Milosa 7 Kosovska Mitrovica 38220 Serbia Univ Nis Fac Elect Engn Aleksandra Medvedeva 14 Nish 18000 Serbia

This study takes into consideration a problem that commonly occurs at the design stage of underground cable lines and that relates to the manner of determining the optimal values for dimensions of cable trench and bedding, interaxial spacings between power cables in flat formation and cable ampacities. The problem is formulated as a non-linear optimisation problem with constraints. The gravitational search algorithm, simulated annealing algorithm and generalised pattern search algorithm are used to solve the problem. The optimal solutions are obtained based on the following three optimisation criteria: (i) minimisation of the total installation costs, i.e. the total costs for producing the cable trench, laying the bedding material, laying the backfill material and installing the cables, (ii) maximisation of the cable ampacity and (iii) a simultaneous application of the minimisation of the total installation costs and the maximisation of the cable ampacity. The constraints on design and control variables are introduced through penalty factors by means of which the objective function is expanded. The procedure is conducted taking into account the effect of drying out of the soil surrounding the cables.

关键词： power cables underground cables search problems nonlinear programming simulated annealing minimisation thermal environment underground power cable line gravitational search algorithm cable trench dimension cable bedding dimension interaxial spacing nonlinear optimisation problem simulated annealing algorithm generalised pattern search algorithm installation cost minimisation backfill material cable ampacity maximisation

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Nonlinear system identification based on ANFIS-Hammerstein model using gravitational search algorithm

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APPLIED INTELLIGENCE 2018年第1期48卷 182-203页

作者： Cuevas, Erik Diaz, Primitivo Avalos, Omar Zaldivar, Daniel Perez-Cisneros, Marco Univ Guadalajara Dept Elect CUCEI Ave Revoluc 1500 Guadalajara Jal Mexico

The identification of real-world plants and processes, which are nonlinear in nature, represents a challenging problem. Currently, the Hammerstein model is one of the most popular nonlinear models. A Hammerstein model involves the combination of a nonlinear element and a linear dynamic system. On the other hand, the Adaptive-network-based fuzzy inference system (ANFIS) represents a powerful adaptive nonlinear network whose architecture can be divided into a nonlinear block and a linear system. In this paper, a nonlinear system identification method based on the Hammerstein model is introduced. In the proposed scheme, the system is modeled through the adaptation of an ANFIS scheme, taking advantage of the similarity between it and the Hammerstein model. To identify the parameters of the modeled system, the proposed approach uses a recent nature-inspired method called the gravitational search algorithm (GSA). Compared to most existing optimization algorithms, GSA delivers a better performance in complex multimodal problems, avoiding critical flaws such as a premature convergence to sub-optimal solutions. To show the effectiveness of the proposed scheme, its modeling accuracy has been compared with other popular evolutionary computing algorithms through numerical simulations on different complex models.

关键词： ANFIS gravitational search algorithm Nature-inspired algorithms

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Improved gravitational search algorithm with crossover

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 66卷 505-516页

作者： Yin, Baoyong Guo, Zhaolu Liang, Zhengping Yue, Xuezhi JiangXi Univ Sci & Technol Sch Sci Ganzhou 341000 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

gravitational search algorithm (GSA), a popular evolutionary computation technique, has been widely employed in data management. However, the basic GSA demonstrates good exploration search but weak exploitation search. To promote the exploitation capability of GSA, this paper introduces a modified GSA with crossover (CROGSA). In its search process, CROGSA executes the crossover-based search scheme to update the position of each solution. Moreover, the crossover-based search scheme takes advantage of the promising knowledge extracted from the global optimal position achieved until now to enhance the exploitation capability. Experiments on a suit of benchmark cases indicate that CROGSA is better than several related optimization approaches in most of the cases. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Global optimization gravitational search algorithm Crossover search strategy

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