# A Metaheuristic Framework with Experience Reuse for Dynamic Multi-Objective Big Data Optimization
## 1 Introduction
Inspired by biological mechanisms such as biological evolution and social interaction, metaheuristic algorithms also have the ability to adapt to environmental changes just like living things, and metaheuristics are reasonable solutions to solve dynamic optimization problems. Generally, metaheuristics can be divided into evolutionary algorithms (EAs) and swarm intelligence (SI), and these algorithms have achieved significant success in combinatorial optimization problems [4; 5; 6], constrained optimization problems [7; 8; 9], and many other complex optimization problems under stationary environments [10; 11; 12]. In contrast, there are few studies on applying metaheuristics to solve DMOPs [13] and even fewer studies on DMBDOPS, though these problems are of great importance in practice. Therefore, it is necessary to investigate how metaheuristics can be applied to solve DMBDOPS. Moreover, metaheuristics have strong adaptation capabilities, but it is difficult for a conventional metaheuristic to track the changing optimum once the algorithm has converged on a solution when addressing dynamic optimization problems [14]. One way to solve this puzzle is to consider every change in the environment as a new optimization problem, and the algorithm has to solve it from scratch to ensure the diversity of solutions. However, this method may not be adaptive to a DMBDOP considering the efficiency and real-time requirements of big data tasks. Hence, designing more efficient problem change response strategies for dynamic algorithms according to the characteristics of DMDDOPs is another essential task. In this paper, we present a metaheuristic software framework named JDBDO, which was written in Java 11 and designed to solve and research DMDBOPs. Our motivation is to provide a software framework to support the research of DMDBOPs, try to solve DMDBOPs using metaheuristic algorithms, and provide as comprehensive test suites as possible. Since DMDBOPs are closely related to big data and the optimization process contains many data operations, Java has natural advantages when using some big data processing engines, and as a compile-based programming language, the execution of algorithms can be relatively fast. In addition, to solve DMDBOPs more efficiently and cope with problem changes, this work proposes an experience reuse method to accelerate the optimization process after the environment changes. This method makes metaheuristics handle the trade-off between exploration and exploitation well when constructing the initial population of the new problem. The source code for a preliminary version of JDBDO is now available at: [[https://github.com/NEUCS/JDBDO](https://github.com/NEUCS/JDBDO)]([https://github.com/NEUCS/JDBDO](https://github.com/NEUCS/JDBDO)) (accessed on 1 May 2023). On the whole, the main new contributions of this work are summarized as follows:  
* A metaheuristic framework based on Java for solving DMDBOPs is proposed, which contains a metaheuristic set including both EAs and SI algorithms. The framework utilizes Apache Spark for implementing parallel computing and stream processing. The proposed framework remedies the current lack of a software framework for DMDBOPs and supports subsequent research on these problems. * Some test suites are developed and embedded in the proposed framework, involving two kinds of DMDBOPs. One is extended from traditional DMOPs by introducing the streaming feature, and another is DMOP with high-dimensional decision space. These test suites aim to offer broader and relatively comprehensive experimental use cases for studies in this field. * An experience reuse method is proposed for enhancing computing efficiency while maintaining the diversity and quality of solutions. This method could measure the degree of change between problems if the optimization algorithm needs to restart. Then, it would decide whether to exploit historical experience or re-explore a new situation. The remainder of this paper is organized as follows. Section 2 introduces related work of this paper.