## 1 Introduction  
Over the last two decades, multi-objective problems (MOPs) have received growing attention because of their wide applications [1; 2; 3; 4; 5; 6; 7]. Many multi-objective evolutionary algorithms (MOEAs) are designed to solve real-world applications. As is well known, however, objective functions of MOPs,often conflict with each other and improvement of one objective may lead to deterioration of another. Therefore, it is difficult to find an optimal solution that can satisfy all optimization objectives. To solve MOPs, researchers usually look for not only a single solution but a set of Pareto optimal solutions [8].  
Many researchers apply Evolutionary Algorithms (EAs) for the MOPs because of the significant advantages of EAs [9, 10, 11, 12, 13, 14]. The most popular MOEAs can be classified into three categories [15]: pareto dominance-based MOEAs, decomposition-based MOEAs, and indicator-based MOEAs. The distinctive features of methods based on pareto dominance, such as the strength Pareto evolutionary algorithm (SPEA)-II, Pareto Envelope-Based Selection Algorithm (PESA)-II and the Fast Non-Dominated Sorting Genetic Algorithm (NSGA-II), are the individual selection method, population diversity preservation mechanism based on fitness sharing, and elite reserved strategy. [PERSON] _et al._[16] proposed a proportional weight specification method to solve multi-objective problems. Later, [PERSON] _et al._ proposed the multi-objective evolutionary algorithm based on decomposition (MOEA/D) [17]. The algorithm decomposes a continuous MOP into several single-objective optimization problems. The population of each subproblem is composed of the best solution found so far. Each subproblem has several neighbors that are defined based on the distances between their corresponding weight vectors. MOEA/D aroused many researchers interest as soon as it was proposed. Several improvements on MOEA/D have been made recently [18, 19, 20, 21, 22]. At the same time, MOEA/D has been successfully applied to a number of application areas [15, 23, 24, 25]. The classic methods of indicator-based are the multiobjective selection based on dominated hypervolume (SMS-EMOA) [26] and the algorithm for fast hypervolume-based many-objective optimization (HypE) [27].  
Inspired by the cloud model, this paper presents a novel evolution approach. We employ cloud particles' evolution in the framework of MOEA based on decomposition (MOEA/D) called multiobjective decomposition evolutionary algorithm based on Cloud Particle Differential Evolution (MOEA/D-CPDE). At each generation, the individual that is the best solution found so far acts as a seed. The seed can be seen as the parent. Every seed evolves into two individuals by a cloud generator. Each of them not only has the characteristics of the seed but also has its own characteristics. Then, the algorithm updates the current individual with the position vector difference of these two individuals. The strategy can improve the diversity of the population and improve the performance of the algorithm.  
The rest of the paper is organized as follows. Section 2 presents the related background including the framework of MOEA/D. Section 3 presents the cloud generator and MOEA/D-CPDE. Section 4 describes the benchmark problems, the parameter settings, performance metrics, and experimental results. Section 5 concludes the paper and points out some further studies.