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Cloud computing, a technology providing flexible and scalable computing resources, faces a critical challenge in task scheduling, directly impacting system performance and customer satisfaction. The task scheduling problem's NP-completeness makes finding solutions difficult. To address this, researchers propose a hybrid algorithm combining the Grey Wolf Optimization Algorithm (GWO) and the Genetic Algorithm (GA). The hybrid GWO-GA algorithm targets multi-objective task scheduling in cloud computing, aiming to minimize makespan, energy consumption, and cost. Enhancements to the proposed algorithm involve leveraging the genetic algorithm's crossover and mutation operator. Furthermore, the GA-based GWO algorithm's faster convergence in large scheduling problems presents an advantage. Evaluation using the Cloudsim toolkit demonstrates the proposed algorithm's efficiency compared to existing methods. We have used both synthetic and real world data set. The results are verified using the Analysis of Variance (ANOVA) statistical tool. Experimental results showcase its effectiveness in minimizing makespan, energy consumption, and computational cost. Particularly, the proposed algorithm outperforms traditional GWO, GA, and PSO algorithms in terms of makespan, cost, and energy consumption, achieving reductions of 19%, 21%, and 15%, respectively, when compared to each approach. Additionally, it yields energy savings of 17%, 19%, and 23% compared to GWO, GA, and PSO, respectively, while reducing total scheduling costs by 13%, 17%, and 22%. These findings demonstrate the efficacy of the proposed algorithm in addressing the task scheduling problem in cloud computing environments. • A hybrid task scheduling strategy based on GA and GWO is proposed. • Introducing mutation and crossover to preserve wolf diversity and boost local search in the algorithm. • A novel fitness function is proposed considering multiple objectives simultaneously. • We have synthetic and real world data set for the simulation. • Results with CloudSim show the enhanced performance of proposed algorithm. [ABSTRACT FROM AUTHOR]

• Paper proposes an optimization framework for deploying green and survivable FiWi. • Various optimization algorithms are implemented for optimizing ONUs positions. • Parameters like energy and survivability are evaluated and analyzed. • Proposed framework offers multiple solutions for green and survivable FiWi network. Fiber Wireless (FiWi) access network also known as hybrid wireless optical broadband access network is a promising modern access technology as it provides high bandwidth, better flexibility, reliability and stability at lower cost. Likewise other access networks, energy saving and survivability are the most important issues in FiWi also. Therefore this paper provides an optimization framework as a comprehensive solution for deployment of green and survivable FiWi network. We optimize ONUs position in the network using different optimization algorithms such as genetic algorithm, teaching learning based optimization algorithm and whale optimization algorithm (Encircling Prey and Spiral Update Positioning). This optimization helps in achieving energy efficiency and survivability by mutually connecting maximum possible ONUs and wireless routers to each other. An extensive simulation is carried out to show the effectiveness of the present work. Simulation results show reduction in number of all time active ONUs, total physical distance among ONUs and associated wireless routers and total active time of the network. At the same time survivability is achieved by increasing average ONU connectivity of wireless routers. A detailed comparison of applied algorithms states that out of different optimization algorithms, genetic algorithm provides best energy efficiency but average survivability. Teaching learning based optimization & whale optimization algorithm (Encircling Prey) are average energy efficient but survivability offered by these algorithms are average and excellent respectively. Similarly whale optimization algorithm (Spiral Update Positioning) provides good energy efficiency but average survivability. Hence as per the need of energy efficiency and survivability, suitable optimization algorithm can be selected for deployment of green and survivable FiWi access network. [ABSTRACT FROM AUTHOR]