Multi-Verse Optimizer: a nature-inspired algorithm for global optimization

Abstract

This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four wellknown algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at

A hybrid SA-MFO algorithm for function optimization and engineering design problems

“…The first experiment aims to determine the optimal parameter value for both T 0 and α. The second experiment aims to evalu- ate and compare the performance of SA-MFO with MFO and five well-known optimization algorithms: particle swarm optimization (PSO) [15], ant bee colony (ABC) [14], moth flame optimization (MFO) [24], multi verse optimization (MVO) [23], and ant lion optimizer (ALO) [22] in solving 23 numerical optimization problems using different statistical measurements. The parameters settings for all meta-heuristic optimization algorithms are shown in Table 2.…”

Performance comparisons of current metaheuristic algorithms on unconstrained optimization problems

“…Main steps of this algorithm are demonstrated in Figure 4. MVO is a novel search and optimization algorithm main inspirations of which are based on three concepts in cosmology: white hole, black hole, and wormhole [8]. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, in optimization respectively.…”

“…In this paper, seven of newest metaheuristic algorithms namely, Ant Lion Optimization (ALO) [4], Dragonfly Algorithm (DA) [5], Grey Wolf Optimization (GWO) [6], Moth-Flame Optimization (MFO) [7], Multi-Verse Optimizer (MVO) [8], Sine Cosine Algorithm (SCA) [9], and Whale Optimization Algorithm (WOA) [10] have been tested on unconstrained benchmark optimization problems and their performances have been reported. Organization of this paper has been as follows: Section 2 gives brief explanations of the current algorithms with pseudo-codes.…”

Polar Particle Swarm Algorithm for Solving Cloud Data Migration Optimization Problem

“…The SCA created multiple initial random candidate solutions and required them to fluctuate outwards or towards the best solution using a mathematical model based on. Mirjalili et al (2016) proposed a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO), based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively.…”