Intelligent Optimization
Principles, Algorithms and Applications
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189 Result(s)
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Article
Open AccessA Multi-strategy Improved Grasshopper Optimization Algorithm for Solving Global Optimization and Engineering Problems
This paper presents a multi-strategy improved grasshopper optimization algorithm (MSIGOA), which aims to address the shortcomings of the grasshopper optimization algorithm (GOA), including its slow convergence...
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Article
Hybrid generative adversarial network based on a mixed attention fusion module for multi-modal MR image synthesis algorithm
Recently, medical image synthesis has attracted the attention of an increasing number of researchers. However, most of current approaches suffer from the loss of multi-modal complementary information and thus ...
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Article
Open AccessPublisher Correction: GOSS: towards generalized open-set semantic segmentation
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Article
Open AccessGOSS: towards generalized open-set semantic segmentation
In this paper, we extend Open-set Semantic Segmentation (OSS) into a new image segmentation task called Generalized Open-set Semantic Segmentation (GOSS). Previously, with well-known OSS, the intelligent agent...
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Article
Foodnet: multi-scale and label dependency learning-based multi-task network for food and ingredient recognition
Image-based food pattern classification poses challenges of non-fixed spatial distribution and ingredient occlusion for mainstream computer vision algorithms. However, most current approaches classify food and...
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Chapter and Conference Paper
Blockchain and OR Based Data Sharing Solution for Internet of Things
Communication between nodes in the Internet of Things (IoT) is still in its infancy, and several contemporary security dangers and issues are impeding data transmission in this process. In this research, we in...
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Chapter
Multimodal Optimization
This chapter will first introduce the definition of multimodal optimization. Next, most representative evolutionary multimodal optimization algorithms, which are also known as niching methods, will be introduc...
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Chapter
Constrained Optimization
In this chapter, we will introduce the concept of (COPs), commonly used constraint-handling techniques based on EAs, and future research on constrained optimization.
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Chapter
Introduction
This chapter briefly introduces the background of optimization. Firstly, the relationship between optimization and machine learning is discussed, and an example of machine learning task is given. Secondly, the...
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Chapter
Exploitation Versus Exploration
This chapter will first introduce the concepts of exploration and exploitation by reviewing several typical optimization algorithms. Next, methods of enhancing exploration or exploitation in EAs will be discus...
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Chapter
Large-Scale Global Optimization
With the development of computing capability and optimization technologies, research on optimization has shifted from simple optimization problems to complex optimization problems. One of the challenges posed ...
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Chapter
Parameter Control and Policy Control
This chapter aims to explain some basic methods for parameter control and strategy control, which have a significant impact on the performance of EAs. Single-parameter-specific methods and multi-parameter ense...
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Book
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Chapter
Robust Optimization
The definition of robust optimization originates from the field of engineering design. Due to the limitation of production technology, the production process is full of uncertain factors, such as the change of...
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Chapter
Expensive Optimization
In this chapter, we will first introduce the concept of expensive optimization. Some surrogate models and model management strategies in surrogate-assisted EAs are then described in detail. Finally, some chall...
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Chapter
Canonical Optimization Algorithms
This chapter introduces canonical optimization algorithms, including numerical optimization methods for continuous optimization problems and state space search methods for discrete optimization problems. Sever...
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Chapter
Popular Evolutionary Computation Algorithms
This chapter introduces several popular EAs, including the four classical paradigms of GA, GP, EP, and ES and also including the emerging mainstream of PSO, DE, EDA, and ACO. GA, GP, PSO, and DE generate offsp...
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Chapter
Multi-objective Optimization
In recent decades, evolutionary multi-objective optimization has attracted a growing interest due to the fact that many real-world applications are (MOPs). This chapter introduces basic concepts regarding mu...
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Chapter
Dynamic Optimization
Dynamic optimization problems (DOPs) have attracted more and more interest in recent years. On the one hand, there are numerous real-world optimization problems that exhibit dynamic characteristics. On the ot...
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Chapter
Real-World Applications
EAs have been widely applied in various fields; in this chapter, results of some of real-world applications from our research group, e.g., the design of antenna, the vehicle routing problem, and the contaminat...
