# A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment
## 2 Related Work  
Several heuristic and metaheuristic algorithms have tried to address workflow scheduling in the cloud environment using different strategies [10; 11; 12; 13; 14]. Critical Path On a Processor (CPOP), Heterogeneous Earliest Finish Time (HEFT) [10], Heterogeneous Critical Parent Trees (HCPT), High Performance Task Scheduling (HPS), Performance Effective Task Scheduling (PETS), Lookahead, and PEFT [5] are some of the well-known list-based scheduling heuristics. All of them attempt to find suitable schedule maps on the basis of some pre-defined rules and problem size. Hence, they are only locally optimal and infeasible for large and complex workflow scheduling problems in the cloud. Recently, [PERSON] and [PERSON] (2018) [15] proposed a model, Dynamic Scheduling of Bag of Tasks based workflows (DSB), for scheduling large and complex scientific workflows on elastic, heterogeneous, scalable, and dynamically provisioned VMs. It minimizes the financial cost under a deadline constraint. However, all of them consider the optimization of a single objective only. The metaheuristic-based techniques are used to find near-optimal solutions for these complex workflow scheduling problems. Recently, a number of nature-inspired metaheuristic-based techniques, such as Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), the Bat Algorithm (BA), Cuckoo Search (CS), Differential Evolution (DE), the Firefly Algorithm (FA), the Genetic Algorithm (GA),Harmony Search (HS), the Immune Algorithm (IA), the League Championship Algorithm (LCA), the Lion Optimization Algorithm (LOA), the Memetic Algorithm (MA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) [16] have been applied in solving the task scheduling problem. A metaheuristic algorithm can be improved in terms of the quality of the solution or convergence speed by combining it with another population-based metaheuristic algorithm or some local search-based metaheuristic algorithm [17]. [PERSON] et al. (2017) [18] proposed a hybrid bio-inspired algorithm for task scheduling and resource management of cloud resources in terms of efficient resource utilization, improved reliability, and reduced average response time. [PERSON] et al. (2015) [19] hybridized a gravitational emulation local search strategy with particle swarm optimization to improve the obtained solution. [PERSON] and [PERSON] (2016) [20] proposed an SA-based SOS in order to improve the convergence rate and quality of solution. The MOP is a very promising direction to tackle the problem of workflow scheduling in the cloud. [PERSON] (2014) [21] used the MOP approach based on a Pareto optimal non-dominated solution for the workflow scheduling problem in the cloud. [PERSON] et al. (2016) [2] proposed an evolutionary multi-objective scheduling for cloud (EMS-C) algorithm to solve the workflow scheduling problem on the IaaS platform. Extensions of HEFT [10], the Pareto Optimal Scheduling Heuristic (POSH) [22], and Multi-Objective Heterogeneous Earliest Finish Time (MOHEFT) [3] were designed to provide users with a set of trade-off optimal solutions for scheduling workflows in the cloud. A multi-objective heuristic algorithm, Min-min based time and cost tradeoff (MTCT), was proposed by [PERSON] et al. (2016) [23]. A scheduling approach, the Balanced and file Reuse-Replication Scheduling (BaRRS) algorithm, was proposed to select the optimal solution based on makespan and cost [24]. However, they focus on only two objectives. Recently, some hybrid multi-objective algorithms have been used by combining the good features of two or more approaches: adaptive hybrid PSO [25], the hybrid multi-objective population migration algorithm [26], Multi-Objective SOS (MOSOS) with an adaptive penalty function [27], non-dominance sort-based Hybrid PSO (HPSO) [28], and Fragmentation-Based Genetic Algorithm (FBGA) [29].