Volcano eruption algorithm for solving optimization problems

Meta-heuristic algorithms have been proposed to solve several optimization problems in different research areas due to their unique attractive features. Traditionally, heuristic approaches are designed separately for discrete and continuous problems. This paper leverages the meta-heuristic algorithm for solving NP-hard problems in both continuous and discrete optimization fields, such as nonlinear and multi-level programming problems through extensive simulations of volcano eruption process. In particular, a new optimization solution named volcano eruption algorithm is proposed in this paper, which is inspired from the nature of volcano eruption. The feasibility and efficiency of the algorithm are evaluated using numerical results obtained through several test problems reported in the state-of-the-art literature. Based on the solutions and number of required iterations, we observed that the proposed meta-heuristic algorithm performs remarkably well to solve NP-hard problem. Furthermore, the proposed algorithm is applied to solve some large-size benchmarking LP and Internet of vehicles problems efficiently.

[1]  Hossam Faris,et al.  Fraud Detection Model Based on Multi-Verse Features Extraction Approach for Smart City Applications , 2019, Smart Cities Cybersecurity and Privacy.

[2]  Seyed Mohammad Mirjalili,et al.  Whale optimization approaches for wrapper feature selection , 2018, Appl. Soft Comput..

[3]  Shuai Li,et al.  Distributed Clustering of Linear Bandits in Peer to Peer Networks , 2016, ICML.

[4]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[5]  Xin-She Yang,et al.  Bat algorithm: literature review and applications , 2013, Int. J. Bio Inspired Comput..

[6]  Mohsen Guizani,et al.  Transmission power adaption scheme for improving IoV awareness exploiting: evaluation weighted matrix based on piggybacked information , 2018, Comput. Networks.

[7]  I. N. Kamalabadi,et al.  A Genetic Approach for Solving Bi-Level Programming Problems , 2013 .

[8]  Ya D Sergeyev,et al.  On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget , 2018, Scientific Reports.

[9]  Amir Hossein Alavi,et al.  Krill herd: A new bio-inspired optimization algorithm , 2012 .

[10]  Amir Hossein Gandomi,et al.  Chaotic gravitational constants for the gravitational search algorithm , 2017, Appl. Soft Comput..

[11]  Muhammad Khurram Khan,et al.  Bat algorithm-based beamforming for mmWave massive MIMO systems , 2020, Int. J. Commun. Syst..

[12]  Shuai Li,et al.  Stochastic Optimization Techniques for Quantification Performance Measures , 2016, KDD 2016.

[13]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[14]  Mohsen Guizani,et al.  Enabling Efficient Coexistence of DSRC and C-V2X in Vehicular Networks , 2020, IEEE Wireless Communications.

[15]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[16]  Andrew Lewis,et al.  Particle Swarm Optimization: Theory, Literature Review, and Application in Airfoil Design , 2019, Nature-Inspired Optimizers.

[17]  Peng Zeng,et al.  Real-Time Density Detection in Connected Vehicles: Design and Implementation , 2018, IEEE Communications Magazine.

[18]  Eghbal Hosseini,et al.  Three new methods to find initial basic feasible solution of transportation problems , 2017 .

[19]  Jin Song Dong,et al.  Genetic Algorithm: Theory, Literature Review, and Application in Image Reconstruction , 2019, Nature-Inspired Optimizers.

[20]  Eghbal Hosseini,et al.  Big Bang Algorithm: A New Meta-heuristic Approach for Solving Optimization Problems , 2017 .

[21]  Shuai Li,et al.  Collaborative Filtering Bandits , 2015, SIGIR.

[22]  Seyed Mohammad Mirjalili,et al.  Ions motion algorithm for solving optimization problems , 2015, Appl. Soft Comput..

[23]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[24]  Andreas Kronz,et al.  Growth of, and diffusion in, olivine in ultra-fast ascending basalt magmas from Shiveluch volcano , 2018, Scientific Reports.

[25]  Kamalrulnizam Abu Bakar,et al.  Intelligent beaconless geographical forwarding for urban vehicular environments , 2012, Wireless Networks.

[26]  Ibrahim Eksin,et al.  A new optimization method: Big Bang-Big Crunch , 2006, Adv. Eng. Softw..

[27]  Yaroslav D. Sergeyev,et al.  GOSH: derivative-free global optimization using multi-dimensional space-filling curves , 2018, J. Glob. Optim..

[28]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[29]  Eghbal Hosseini,et al.  Laying Chicken Algorithm: A New Meta-Heuristic Approach to Solve Continuous Programming Problems , 2017 .

[30]  Eghbal Hosseini,et al.  Solving Linear Tri-level Programming Problem Using Heuristic Method Based on Bi-section Algorithm , 2017 .

[31]  Erik Valdemar Cuevas Jiménez,et al.  A swarm optimization algorithm inspired in the behavior of the social-spider , 2013, Expert Syst. Appl..

[32]  Jacqueline T. Salzer,et al.  of Geophysical Research : Solid Earth Volcano dome dynamics at Mount St . Helens : Deformation and intermittent subsidence monitored by seismicity and camera imagery pixel offsets , 2016 .

[33]  Yu Liu,et al.  A New Bio-inspired Algorithm: Chicken Swarm Optimization , 2014, ICSI.

[34]  M. H. Afshar,et al.  A novel hybrid cellular automata–linear programming approach for the optimal sizing of planar truss structures , 2014 .

[35]  Andrew Lewis,et al.  Autonomous Particles Groups for Particle Swarm Optimization , 2014 .

[36]  Marco Laumanns,et al.  Scalable multi-objective optimization test problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[37]  Kaisa Miettinen,et al.  A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization , 2018, IEEE Transactions on Evolutionary Computation.

[38]  Hanif D. Sherali,et al.  Linear Programming and Network Flows , 1977 .

[39]  G. Di Caro,et al.  Ant colony optimization: a new meta-heuristic , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[40]  E. Hosseini.  Presentation and Solving Non-Linear Quad-Level Programming Problem Utilizing a Heuristic Approach Based on Taylor Theorem , 2018 .

[41]  Shuai Li,et al.  Mining λ-Maximal Cliques from a Fuzzy Graph , 2016 .

[42]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[43]  Kayhan Zrar Ghafoor,et al.  Quality of Service Aware Routing Protocol in Software-Defined Internet of Vehicles , 2019, IEEE Internet of Things Journal.