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.
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The source code of our porposed VEA is "https://github.com/eghbal11/Eghbal/blob/master/VEA.m".
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Hosseini, E., Sadiq, A.S., Ghafoor, K.Z. et al. Volcano eruption algorithm for solving optimization problems. Neural Comput & Applic (2020). https://doi.org/10.1007/s00521-020-05124-x
- Constrained optimization
- Volcano eruption algorithm (VEA)
- Bi-level optimization