Volcano eruption algorithm for solving optimization problems

This is an accepted manuscript of an article published by Springer in Neural Computing and Applications on 30/06/2020, available online at https://doi.org/10.1007/s00521-020-05124-x The accepted version of the publication may differ from the final published version.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 (VEA) 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-theart 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 (IoV) problems efficiently

A hybrid cuckoo search algorithm for cost optimization of mechanically stabilized earth walls

Having a wide range of applications in civil engineering practice, Mechanically Stabilized Earth Walls (MSEWs) are regarded as efficient and reliable alternatives to the conventional retaining structure types. As is often the case in engineering, the performance and cost-effectiveness of these structures rely on robust design strategies, which must be proficient to yield optimal solutions in multimodal spaces. While the inherent characteristics of engineering problems often render the design a challenging task, metaheuristic algorithms are suitable options provided that problem-specific considerations and modifications are implemented. In this regard, Cuckoo Search (CS) and its variants are successful in many engineering applications. In the present study, CS is adopted to optimize the reinforcement type, length, and layout of MSEWs and a hybrid CS (HCSDE) variant based on Differential Evolution formulation is developed to further enhance the search capability of the algorithm. The proposed algorithm is applied to various MSEW design benchmarks and comparatively evaluated with respect to well-established methods such as Genetic Algorithm and Particle Swarm Optimization. The results of the study indicate that CS is competent for the problem and the capability of the algorithm can be further enhanced through the proposed adaptations in HCSDE. The improved solutions of HCSDE compared to the other optimization methods highlight the proposed formulation as a promising algorithm for practical implementations