Metaheuristic algorithms play a vital role in addressing a wide range of real-world problems by overcoming hardware and computational constraints. The Chameleon Swarm Algorithm (CSA) is a modern metaheuristic algorithm that uses how chameleons act. To improve the capabilities of the CSA, this work proposes a modified version of the Chameleon Swarm Algorithm to find better optimal solutions applicable to various application areas. The effectiveness of the proposed algorithm is assessed using 97 typical benchmark functions and three real-world engineering design problems. To validate the efficacy of the proposed algorithm, it has been compared to a number of well-known and widely-used classical algorithms, the Gravitational Search Algorithm, the Earthworm Optimization. The proposed modified Chameleon Swarm Algorithm using Morlet wavelet mutation and Lévy flight (mCSAMWL) is superior to existing algorithms for both unimodal and multimodal functions, as demonstrated by Friedman’s mean rank test as well as three real world engineering design problems. Five performance metrics—average energy consumption, total energy consumption, total residual energy, dead node and cluster head frequency are taken into consideration when evaluating the performances against state-of-the-art algorithms. For nine different simulation scenarios, the proposed algorithm mCSAMWL outperforms the Atom Search Optimization (ASO), Hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSO-GWO), Bald Eagle Search Algorithm (BES), the African Vulture Optimization Algorithm (AVOA), and the Chameleon Swarm Algorithm (CSA) in terms of average energy consumption and total energy consumption by 50.9%, 52.6%, 45%, 42.4%, 50.1% and 51.4%, 53.3%, 45.6%, 42.4%, 50.7%.

The convenience and cost-effectiveness offered by cloud computing have attracted a large customer base. In a cloud environment, the inclusion of the concept of virtualization requires careful management of resource utilization and energy consumption. With a rapidly increasing consumer base of cloud data centers, it faces an overwhelming influx of Virtual Machine (VM) requests. In cloud computing technology, the mapping of these requests onto the actual cloud hardware is known as VM placement which is a significant area of research. The article presents the Dragonfly Algorithm integrated with Modified Best Fit Decreasing (DA-MBFD) is proposed to minimize the overall power consumption and the migration count. DA-MBFD uses MBFD for ranking VMs based on their resource requirement, then uses the Minimization of Migration (MM) algorithm for hotspot detection followed by DA to optimize the replacement of VMs from the overutilized hosts. DA-MBFD is compared with a few of the other existing techniques to show its efficiency. The comparative analysis of DA-MBFD against E-ABC, E-MBFD, and MBFD-MM shows %improvement reflecting a significant reduction in power consumption 8.21 %, 8.6 %, 6.77 %, violations in service level agreement from 9.25 %, 6.98 %–7.86 % and number of migrations 6.65 %, 8.92 %, 7.02 %, respectively.