# A Metaheuristic Framework with Experience Reuse for Dynamic Multi-Objective Big Data Optimization
## 2 Related Work
### Metaheuristics in DMOPs and DMDDOPs  
In MOPs, several conflicting objectives need to be optimized simultaneously, and problems are continuous during the optimization process. Metaheuristic algorithms have been applied to MOPs with remarkable success. However, many other real-world problems are dynamic due to objective functions, constraints, and other parameters that may change over time [13]. These problems are defined as DMOPs. A key challenge in a DMOP is making proper responses when detecting problem changes because the convergence of the algorithm may decrease diversity, which directly impacts the adaptability of the algorithm to problem changes and the quality of new solutions. In [15], a diversity-based strategy is designed by using the new problem to evaluate both parent and offspring solutions. In each optimization run, \(\zeta\)% of the new population is replaced by randomly created or mutated solutions, aiming to increase diversity. In addition, prediction-based methodsare also widely used in dealing with DMOPs, in which the Pareto front (PF) or Pareto set (PS) would be different when the changes occur. Different prediction strategies are used in EAs to predict the new location of the PF or PS. For instance, [PERSON] and [PERSON] [16] combined a queuing multi-objective optimizer with an autoregressive model (a forecasting model) for predicting the location of the optimal PS and helping to find the following PS. More recently, researchers have been trying to apply artificial intelligence techniques to prediction. Ref. [17] presented an ensemble learning-based prediction strategy that takes advantage of historical information to train four base prediction models, and this strategy helps EAs reinitialize a new population after a change is detected. In [18], a regression transfer learning prediction-based EA is proposed to generate an excellent initial population to accelerate the evolutionary process and improve the evolutionary performance in solving DMOPs. The field of dynamic multi-objective optimization is closely related to EAs. However, it has been attracting more and more interest in applying SI algorithms to cope with DMOPs. Nevertheless, the research on this aspect is still limited, and some of the current works are reviewed here. In [19], different multi-objective ant colony optimization (ACO) algorithms have been applied to a dynamic multi-objective railway junction rescheduling problem, and this algorithm performs well when the changes in the problem are large and frequent. In [20], a dynamic multi-objective particle swarm optimization (PSO) algorithm based on adversarial decomposition and neighborhood evolution was proposed. The proposed algorithm utilizes a novel particle update strategy to guide evolution and improve the diversity of solutions. [PERSON] et al. [21] proposed a multi-agent-based artificial bee colony (ABC) algorithm for adjusting cooperation among autonomous rescue agents in dynamic disaster environments. Literature [22] presented a multi-objective cat swarm optimization (CSO) algorithm with the Borda count method for DMOPs that could effectively track the time-varying optimal PFs. Research in big data optimization mainly aims to solve stationary problems with many objectives and numerous decision variables in recent years. From this perspective, some works have improved the traditional metaheuristics and realized parallel computing using big data analytics tools. Ref. [23] gives a detailed review based on the application of metaheuristic algorithms to big data optimization. In [24], a hybrid multi-objective firefly algorithm (FA) is proposed, which performed well on the BigOpt2015 benchmark in the CEC2015 Optimization of Big Data Competition. [PERSON] et al. [25] presented an improved NSGA-III algorithm with an adaptive mutation operator for big data optimization problems, which outperforms many NSGA-III variants. In [26], parallel hybrid metaheuristics are presented to deal with large-scale optimization in big data statistical analysis. [PERSON] et al.