A Brief Survey on Intelligent Swarm-Based Algorithms for Solving Optimization Problems

This chapter presents an overview of optimization techniques followed by a brief survey on several swarm-based natural inspired algorithms which were introduced in the last decade. These techniques were inspired by the natural processes of plants, foraging behaviors of insects and social behaviors of animals. These swam intelligent methods have been tested on various standard benchmark problems and are capable in solving a wide range of optimization issues including stochastic, robust and dynamic problems

Tomato Flower Detection and Three-Dimensional Mapping for Precision Pollination

It is estimated that nearly 75% of major crops have some level of reliance on pollination. Humans are reliant on fruit and vegetable crops for many vital nutrients. With the intensification of agricultural production in response to human demand, native pollinator species are not able to provide sufficient pollination services, and managed bee colonies are in decline due to colony collapse disorder, among other issues. Previous work addresses a few of these issues by designing pollination systems for greenhouse operations or other controlled production systems but fails to address the larger need for development in other agricultural settings with less environmental control. In response to this crisis, this research aims to act as a vital first step towards the development of a more robust autonomous pollination system for agricultural crop production. The main objective of this research is to develop a flower detection and mapping system for a field crop setting. This research presents a method to detect and localize tomato flowers within a three-dimensional (3D) region. Tomato plants were grown in a raised-bed garden where images were collected of the overhead view of the plants. Images were then stitched together using a photogrammetry technique, accomplished by the Pix4Dmapper software, to form an orthomosaic and 3D representation of the raised-bed garden from a high spatial resolution aerial view. Various machine learning architectures were trained to detect tomato flowers from overhead images and were then tested on the orthomosaic images produced by the Pix4D software. The coordinates of the detected flowers in the orthomosaic were then compared to the 3D model representation to find approximate 3D coordinates for each of the flowers relative to a predefined origin. This research serves as a first step in autonomous pollination by presenting a way for machine vision and machine learning to be used to identify the presence and location of flowers on tomato crops. Future work will aim to expand flower detection to other crops varieties in varying field conditions

HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images

Recently, a novel virus called COVID-19 has pervasive worldwide, starting from China and moving to all the world to eliminate a lot of persons. Many attempts have been experimented to identify the infection with COVID-19. The X-ray images were one of the attempts to detect the influence of COVID-19 on the infected persons from involving those experiments. According to the X-ray analysis, bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities can be caused by COVID-19 — sometimes with a rounded morphology and a peripheral lung distribution. But unfortunately, the specification or if the person infected with COVID-19 or not is so hard under the X-ray images. X-ray images could be classified using the machine learning techniques to specify if the person infected severely, mild, or not infected. To improve the classification accuracy of the machine learning, the region of interest within the image that contains the features of COVID-19 must be extracted. This problem is called the image segmentation problem (ISP). Many techniques have been proposed to overcome ISP. The most commonly used technique due to its simplicity, speed, and accuracy are threshold-based segmentation. This paper proposes a new hybrid approach based on the thresholding technique to overcome ISP for COVID-19 chest X-ray images by integrating a novel meta-heuristic algorithm known as a slime mold algorithm (SMA) with the whale optimization algorithm to maximize the Kapur's entropy. The performance of integrated SMA has been evaluated on 12 chest X-ray images with threshold levels up to 30 and compared with five algorithms: Lshade algorithm, whale optimization algorithm (WOA), FireFly algorithm (FFA), Harris-hawks algorithm (HHA), salp swarm algorithms (SSA), and the standard SMA. The experimental results demonstrate that the proposed algorithm outperforms SMA under Kapur's entropy for all the metrics used and the standard SMA could perform better than the other algorithms in the comparison under all the metrics

A New Manufacturing Service Selection and Composition Method Using Improved Flower Pollination Algorithm

With an increasing number of manufacturing services, the means by which to select and compose these manufacturing services have become a challenging problem. It can be regarded as a multiobjective optimization problem that involves a variety of conflicting quality of service (QoS) attributes. In this study, a multiobjective optimization model of manufacturing service composition is presented that is based on QoS and an environmental index. Next, the skyline operator is applied to reduce the solution space. And then a new method called improved Flower Pollination Algorithm (FPA) is proposed for solving the problem of manufacturing service selection and composition. The improved FPA enhances the performance of basic FPA by combining the latter with crossover and mutation operators of the Differential Evolution (DE) algorithm. Finally, a case study is conducted to compare the proposed method with other evolutionary algorithms, including the Genetic Algorithm, DE, basic FPA, and extended FPA. The experimental results reveal that the proposed method performs best at solving the problem of manufacturing service selection and composition

A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems

Image segmentation is considered a crucial step required for image analysis and research. Many techniques have been proposed to resolve the existing problems and improve the quality of research, such as region-based, threshold-based, edge-based, and feature-based clustering in the literature. The researchers have moved toward using the threshold technique due to the ease of use for image segmentation. To find the optimal threshold value for a grayscale image, we improved and used a novel meta-heuristic equilibrium algorithm to resolve this scientific problem. Additionally, our improved algorithm has the ability to enhance the accuracy of the segmented image for research analysis with a significant threshold level. The performance of our algorithm is compared with seven other algorithms like whale optimization algorithm, bat algorithm, sine–cosine algorithm, salp swarm algorithm, Harris hawks algorithm, crow search algorithm, and particle swarm optimization. Based on a set of well-known test images taken from Berkeley Segmentation Dataset, the performance evaluation of our algorithm and well-known algorithms described above has been conducted and compared. According to the independent results and analysis of each algorithm, our algorithm can outperform all other algorithms in fitness values, peak signal-to-noise ratio metric, structured similarity index metric, maximum absolute error, and signal-to-noise ratio. However, our algorithm cannot outperform some algorithms in standard deviation values and central processing unit time with the large threshold levels observed

INCUBATION OF METAHEURISTIC SEARCH ALGORITHMS INTO NOVEL APPLICATION FIELDS

Several optimization algorithms have been developed to handle various optimization issues in many fields, capturing the attention of many researchers. Algorithm optimizations are commonly inspired by nature or involve the modification of existing algorithms. So far, the new algorithms are set up and focusing on achieving the desired optimization goal. While this can be useful and efficient in the short term, in the long run, this is not enough as it needs to repeat for any new problem that occurs and maybe in specific difficulties, therefore one algorithm cannot be used for all real-world problems. This dissertation provides three approaches for implementing metaheuristic search (MHS) algorithms in fields that do not directly solve optimization issues. The first approach is to study parametric studies on MHS algorithms that attempt to understand how parameters work in MHS algorithms. In this first direction, we choose the Jaya algorithm, a relatively recent MHS algorithm defined as a method that does not require algorithm-specific control parameters. In this work, we incorporate weights as an extra parameter to test if Jaya’s approach is actually "parameter-free." This algorithm’s performance is evaluated by implementing 12 unconstrained benchmark functions. The results will demonstrate the direct impact of parameter adjustments on algorithm performance. The second approach is to embed the MHS algorithm on the Blockchain Proof of Work (PoW) to deal with the issue of excessive energy consumption, particularly in using bitcoin. This study uses an iterative optimization algorithm to solve the Traveling Salesperson Problem (TSP) as a model problem, which has the same concept as PoW and requires extending the Blockchain with additional blocks. The basic idea behind this research is to increase the tour cost for the best tour found for n blocks, extended by adding one more city as a requirement to include a new block in the Blockchain. The results reveal that the proposed concept can improve the way the current system solves complicated cryptographic problems Furthermore, MHS are implemented in the third direction approach to solving agricultural problems, especially the cocoa flowers pollination. We chose the problem in pollination in cacao flowers since they are distinctive and different from other flowers due to their small size and lack of odor, allowing just a few pollinators to successfully pollinate them, most notably a tiny midge called Forcipomyia Inornatipennis (FP). This concept was then adapted and implemented into an Idle-Metaheuristic for simulating the pollination of cocoa flowers. We analyze how MHS algorithms derived from three well-known methods perform when used to flower pollination problems. Swarm Intelligence Algorithms, Individual Random Search, and Multi-Agent Systems Search are the three methodologies studied here. The results shows that the Multi-Agent System search performs better than other methods. The findings of the three approaches reveal that adopting an MHS algorithms can solve the problem in this study by indirectly solving the optimization problem using the same problem model concept. Furthermore, the researchers concluded that parameter settings in the MHS algorithms are not so difficult to use, and each parameter can be adjusted to solve the real-world issue. This study is expected to encourage other researchers to improve and develop the performance of MHS algorithms used to deal with multiple real-world problems.九州工業大学博士学位論文 学位記番号: 情工博甲第367号 学位授与年月日: 令和4年3月25日1 Introduction|2 Traditional Metaheuristic Search Optimization|3 Parametric Study of Metaheuristic Search Algorithms|4 Embedded Metaheuristic Search Algorithms for Blockchain Proof-of-Work|5 Idle-Metaheuristic for Flower Pollination Simulation|6 Conclusion and Future Works九州工業大学令和3年