# A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms
## 1 Introduction
Since 2003, an EMO algorithm has been developing continuously, and study in the frontier area of evolutionary multi-objective optimization has taken on new characteristics. Several novel algorithm frameworks have been developed as a result of the introduction of certain new evolutionary mechanisms to the discipline of evolutionary multi-objective optimization to more efficiently handle the high-dimensional multi-objective optimization issue [17]. For instance, the multi-objective particle swarm optimization (MOPSO) was suggested by [PERSON] and [PERSON] using particle swarm optimization [18], and the non-dominated neighbor immune algorithm (NNIA) was propounded by [PERSON] et al. using the immune algorithm [19] The multi-objective evolutionary algorithm using decomposition (MOEA/D), which merges evolutionary algorithms with classical mathematical programming techniques, was presented by [PERSON] and [PERSON] [20]. Any new technology arises and develops from its application, otherwise, the new technology would be lifeless. To solve the problem of multiclass pattern discrimination in machine learning, [PERSON] designed and implemented LS2 based on LS1 using the vector evaluation genetic algorithm in 1985 [4]. Since 1990, MOEA has been widely used in various industries, such as environmental and resource allocation, electrical and electronic engineering, communication and networking, robotics, aerospace, municipal construction, transportation, mechanical design and manufacturing, management engineering, finance, and other scientific research. As the use of MOEA becomes more widespread, its classification and applicability to engineering problems become increasingly important [21]. Nowadays, research on the application of MOEA is one of the most popular topics in the research community. About 50% of the papers focus on solving real-world application problems, which is one of the major research areas for the vitality of MOEA [22]. Someexamples include multiobjective optimal control for the wastewater treatment process [23], data mining [24; 25], mechanical design [26], mobile network planning [27], portfolio management [28], design of central neural motion controllers for humanoid robots [29], optimal design of perimeter rocket engines [30], QoS routing [31], logistics distribution [32; 33], logic circuit design [34], multi-sensor multi-objective tracking data association [35], underwater robot motion planning [36], shop floor scheduling problems [37], design problems for urban water management systems [38], and communication and network optimization [39; 40]. The objective of studying MOEA is to apply MOEA to better solve practical problems. Therefore, research on the application of MOEA in various fields is most valuable and meaningful. Within the growing body of research on this field all over the world, many scholars are writing summaries of the field of evolutionary computation algorithms. In recent years, the rapid development of evolutionary multi-objective optimization has warranted a comprehensive survey of this field, as well as analysis and summary [41]. Many other surveys or reviews in the literature are very novel and worth learning from. These have been organized and categorized into Table 1 for better analysis. These surveys can be broadly classified into four categories. The first one is a specialized classification of algorithms that use specific techniques. The second one is a classification of algorithms differently based on certain characteristics. The third one is a survey of algorithms for the problem to be solved. The fourth is a survey of algorithms developed according to time. Our survey can be classified as falling into the second category because we classify representative MOEAs from different MOEA search strategies. However, there is still some difference between them, and while classifying the algorithms, we are also investigating the search strategy as a research direction.