Volcano eruption algorithm for solving optimization problems

Abstract

Due to certain attractive features of meta-heuristics, these algorithms have gained popularity in solving optimization problems and have been considered by many researchers.Because meta-heuristics solve just some of optimization problems, so proposing a meta-heuristic to solve all optimization problems in both kinds of discrete and continous problems could be significant. This paper proposes an algorithm based on simulation of volcano eruption process, which solves NP-hard problems such as non-linear and multi-level programming problems. The algorithm's feasibility and efficiency is then evaluated by applying it to solve a number of test problems reported in the literature. Based on the solutions and number of required iterations, it is concluded that the proposed meta-heuristic algorithm performs remarkably well. Finally, the proposed algorithm solves some large size benchmark linear programming and also the routing problems in the Internet of Vehicles (IoV).

Optimizing Hybrid Renewable Energy Systems:Novel Models and Heuristic Algorithms

“…Numerous machine learning techniques, heuristics, and meta-heuristic algorithms have been proposed in the literature to address a wide array of optimization problems [50], [51], [52], [53], [54], [55], [56], [57], [58], and [59]. However, the inherent complexity of multi-objective optimization problems requires the creation of hybrid algorithms.…”

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

“…( 2009 ) 510 Virus Spread Optimization (VSO) Li and Tam ( 2020 ) 511 Volcano Eruption Algorithm (VCA) Hosseini et al. ( 2021 ) 512 Volleyball Premier League Algorithm (VPL) Moghdani and Salimifard ( 2018 ) 513 Vortex Search Algorithm (VS) Doğan and Ölmez ( 2015 ) 514 War Strategy Optimization (WSO) Ayyarao et al. ( 2022 ) 515 Wasp Swarm Optimization (WSO) Pinto et al.…”

Optimizing Gradient Methods for IoT Applications

“…The constraints are Signal to Interference and Noise Ratio threshold( ℎ) for finding more trustworthy and conjunct route. Volcano Eruption Algorithm (VEA) [18] has been implemented for finding optimal route from origin to destination. Table IV shows the comparison of results and the rate of improvement for GSA and VEA.…”

“…Additionally, researchers have solved optimization problems using simulation of several algorithms based on the behavior of animals and insects, natural phenomena, or scientific theories [15]- [25]. Some of these proposed algorithms are: particle swarm optimization (PSO) [15], artificial bee colony algorithm [16], krill herd algorithm [17], laying chicken algorithm (LCA) [13], volcano eruption algorithm (VEA) [18], multiverse algorithm (MVA) [19], Covid-19 optimizer algorithm (CVA) [20] and grey wolf optimizer [21], bat algorithm [22], social spider optimization [23], chicken swarm optimization (CSO) [24], Fire Fly (FF) [25]. Recently, an algorithm based on a hidden Markov model and an ant colony optimization (ACO) has been proposed to answer the service composition issue by enhancing the QoS [26], an agent-based algorithm for achieving a distributed resources organization in an IoT environment [27] and a secure outsourcing algorithm of bi-linear pairings, which does not require pre-computations has been proposed [28].…”