Swarm Intelligence Algorithms and Their Applications

Background and objectives: Algorithms inspired by the behavior of living organisms have been developed to apply principles from nature to solve problems in various aspects of daily life, e.g., in science, medicine, engineering, technology, and business management. The developed algorithms follow two main ideas. First, evolutionary algorithms use Darwin's principle of natural selection to choose the most suitable organisms in an unforgiving environment. Second, Swarm Intelligence Algorithms mimic the collaborative behavior of groups of living things, such as flocks of birds or schools of fish. The applications of these algorithms include medical data analysis for disease diagnosis, resource allocation planning in construction projects, traffic system design for improved safety, genetic data analysis to find relationships between genes and diseases, and business data analysis to predict customer buying behavior. These algorithms help increase efficiency, accuracy, and sustainability in solving various problems by applying principles inspired by nature. This article aims to explain working principles and performance, the advantages, disadvantages, and applications of Swarm Intelligence algorithms. Methodology: The main research steps were the following: 1. Studying and collecting research documents on 10 algorithms: Ant Colony Optimization, Artificial Bee Colony Algorithm, Bat Algorithm, Particle Swarm Optimization, Flower Pollination Algorithm, Weed Optimization Algorithm, Bee Colony Optimization, Cuckoo Search Algorithm, Bird Flocking Algorithm, and Chicken Swarm Optimization. 2. Analysis of working principles and efficiency of 10 algorithms. 3. Analyzing the advantages and disadvantages of the 10 algorithms. 4. Summarizing the applications of the 10 algorithms. 5. Research results and conclusions. Key findings: This article summarizes the applications of algorithms inspired by the behavior of living organisms to solve various daily life problems in fields such as science, medicine, engineering, technology, and business management. It also aims to explain the advantages and disadvantages of these algorithms in solving complex problems in various aspects of daily life, especially to efficiently achieve sustainable development in society and business. Some applications are such as the following: using intelligent algorithms in medical data analysis for disease diagnosis and environmental management; applying algorithms to analyze medical images for efficient diagnosis and treatment; utilizing algorithms in engineering and technology for infrastructure planning and design to enhance safety and operational efficiency; and using algorithms in business management for data analysis to predict customer behavior and marketing strategies. Implications: Swarm Intelligence algorithms, inspired by the collective behavior of living organisms, are valuable across various fields. In science, they can be utilized to locate underground oil or mineral resources through a network of sensors that collaborate to enhance efficiency in resource detection. In medicine, they aid in analyzing medical images for disease diagnosis and in complex surgeries by directing the movement of surgical robots, emulating precise, cooperative animal actions. In engineering, swarm algorithms improve intelligent traffic systems, simulating vehicle flows to alleviate congestion and plan safer routes. For technology, swarm-inspired collaborative robots excel in manufacturing, coordinating seamlessly with one another in intricate production lines. In agriculture, they are employed for managing smart farms and controlling drones for pest management. In business, swarm algorithms assist in analyzing consumer behavior by examining purchasing patterns, enabling accurate forecasting and adjustments to marketing strategies to better meet customer needs. Conclusions: This review found that Swarm Intelligence algorithms were that the 10 swarm intelligence algorithms have different working principles and performance when applied in various tasks, along with their own advantages and disadvantages and limitations in solving problems, with some algorithms being more effective in certain areas. This article also concludes that some types of algorithms can solve complex problems and can be applied in 5 different areas comprehensively. This review may be beneficial to algorithm developers and users alike.