Convergence Analysis of Swarm Intelligence Metaheuristic Methods

Swarm Intelligence, Focus on Ant and Particle Swarm Optimization, 2007

Optimization problems appear in many fields, as various as identification problems, supervised learning of neural networks, shortest path problems, etc. Metaheuristics [22] are a family of optimization algorithms, often applied to "hard" combinatorial problems for which no more efficient method is known. They have the advantage of being generic methods, thus do not require a complex tuning for each problem, and can be used as a kind of "black boxes". Recall that, generally, optimization algorithms search for a point into the search space, so as to optimize (i.e., minimize or maximize) the objective function (also called fitness or goal function). Metaheuristics are often divided into two sets: 1. Algorithms handling a single point, making it evolve towards a solution. 2. Algorithms handling a population, i.e., a finite set of points, and computing a new population at each iteration. An essential observation is that the population of the second category is a stochastic sampling of the objective function. Although those classes are not disjoint (an algorithm can belong to both classes, according to the point of view), we only consider population metaheuristics, which are simply referred as metaheuristics hereafter. An important contribution in this domain comes from the theory of self-organization [10, p.8], which allows to analyze the properties of several metaheuristics stemming from realworld metaphors, often biological ones. This theory (notably studied except the biology [47]) describes the conditions of appearance of complex phenomena from distributed systems, the agents of which are the object of simple, but numerous interactions. The theory puts in front concepts such as communication, feedback, amplification of fluctuations and emergence. In the metaheuristics field, swarm intelligence was so explicitely used on two main fronts: via an approach "self-organized systems" (having given place to ant colony algorithms) and via an approach "socio-cognitive systems" (having led to the particle swarm optimization). We suggest putting the theory of the swarm intelligence in connection with the concept of adaptive learning search, which tries to describe key points of modern metaheuristics, notably by insisting on the role of the learning and the mechanisms of intensification and diversification. More generally, we think that the theory of self-organization combined with the adaptive learning search gives keys to design the basic components of metaheuristics, recovering from swarm intelligence.

Swarm Agents are known for their cooperative and collective behavior and operate in decentralized manner which is regarded as Swarm Intelligence. Various techniques like Ant Optimization, Wasp, Bacterial Foraging, PSO, etc., are proposed and implemented in various real-time applications to provide solutions to various real-time problems especially in optimization. The aim of this paper to present ABC algorithm in a comprehensive manner. The ABC-based SI technique proposed has demonstrated that it has superior edge in solving all types of unconstrained optimization problems. Many researchers have fine-tuned the basic algorithm and proposed different ABC based algorithms. The result show that still lots of work is required mathematically and live implementation in order to enable ABC algorithm to be applied to constrained problems for effective solutions.

2012

Swarm Intelligence is the most studied area by several researchers it is the part of AI based on the study of actions of individuals in various decentralized system. The BCO metaheuristics has been introduced fairly recently as a new direction in the field of Swarm Intelligence. Artificial Bee represents agents which collaboratively solve complex combinatorial optimization problem. It was introduced in 2005 and has been applied to solve different optimization problems in various areas. BCO is a benchmark system which shows the team work and applied in various optimization problem. Keywords—Swarm Intelligence (SI), Bee Colony Optimization (BCO), Artificial Bee Colony (ABC).

2012

Swarm-based metaheuristic algorithms inspired from swarm systems in nature have produced remarkable results while solving complex optimization problems. This is due to their capability of decentralized control of search agents able to explore search environment more effectively. The large number of metaheuristics sometimes puzzle beginners and practitioners where to start with. This experimental study covers 10 swarm-based metaheuristic algorithms introduced in last decade to be investigated on their performances on 12 test functions of high dimensions with diverse features of modality, scalability, and valley landscape. Based on simulations, it can be concluded that firefly algorithm outperformed rest of the algorithms while tested unimodal functions. On multimodal functions, animal migration algorithm produced outstanding results as compared to rest of the methods. In future, further investigation can be conducted on relating benchmark functions to real-world optimization problem so that metaheuristic algorithms can be grouped according to suitability of problem characteristics.

Artificial Bee Colony Algorithm (ABC) is nature-inspired metaheuristic, which imitates the foraging behavior of bees. ABC as a stochastic technique is easy to implement, has fewer control parameters, and could easily be modify and hybridized with other metaheuristic algorithms. Due to its successful implementation, several researchers in the optimization and artificial intelligence domains have adopted it to be the main focus of their research work. Since 2005, several related works have appeared to enhance the performance of the standard ABC in the literature, to meet up with challenges of recent research problems being encountered. Interestingly, ABC has been tailored successfully, to solve a wide variety of discrete and continuous optimization problems. Some other works have modified and hybridized ABC to other algorithms, to further enhance the structure of its framework. In this review paper, we provide a thorough and extensive overview of most research work focusing on the application of ABC, with the expectation that it would serve as a reference material to both old and new, incoming researchers to the field, to support their understanding of current trends and assist their future research prospects and directions. The advantages, applications and drawbacks of the newly developed ABC hybrids are highlighted, critically analyzed and discussed accordingly.

—In this paper an overview of the areas where the Bee Colony Optimization (BCO) and its variants are applied have been given. Bee System was identified by Sato and Hagiwara in 1997 and the Bee Colony Optimization (BCO) was identified by Lucic and Teodorovic in 2001. BCO has emerged as a specialized class of Swarm Intelligence with bees as agents. It is an emerging field for researchers in the field of optimization problems because it provides immense problem solving scope for combinatorial and NP-hard problems. BCO is one of the benchmark systems portraying team work, collaborative work. BCO is a bottom-up approach of modeling where agents form global solution by optimizing the local solution.

Computación y Sistemas, 2016

The Artificial Bee Colony (ABC) algorithm is a popular swarm based algorithm inspired by the intelligent foraging behavior of honey bees. In the past, many swarm intelligence based techniques were introduced and proved their effective performance in solving various optimization problems. The exploitation of food sources is performed by onlooker bees in accordance with a proportional selection scheme that can be further modified to avoid such shortcomings as population diversity and premature convergence. In this paper, different selection schemes, namely, tournament selection, truncation selection, disruptive selection, linear dynamic scaling, linear ranking, sigma truncation, and exponential ranking have been used to analyze the performance of the ABC algorithm by testing on standard benchmark functions. From the simulation results, the schemes other than the standard ABC prove their efficient performance.

Journal of Electronics, Electromedical Engineering, and Medical Informatics (JEEEMI), 2021

Swarm intelligence (SI), an important aspect of artificial intelligence, is increasingly gaining popularity as more and more high-complexity challenges necessitate solutions that are sub-optimal but still feasible in a fair amount of time. Artificial intelligence that mimics the collective behavior of a group of animals is known as swarm intelligence. Attempting to survive. Optimization contributes to optimal resource management by way of efficient and effective problem-solving. Engineers' attention has been driven to more effective and scalable metaheuristic algorithms as a result of the complicated optimization issues. It is primarily influenced by biological systems. The main aim of our article is to find out more about the guiding principle, classify possible implementation areas, and include a thorough analysis of several SI algorithms. Swarms can be observed in ant colonies, fish schools, bird flocks, among other fields. During this article, we will look at some Swarm instances and their behavior. The authors see many Swarm Intelligence systems, like Ant colony Optimization, which explains ant activity, nature, and how they conquer challenges; in birds, we see Particle Swarm Optimization is a swarm intelligence-based optimization technique, and how the locations must be positioned based on the three concepts. The Bee Colony Optimization follows, and explores the behavior of bees, their relationships, as well as movement and how they work in a swarm. This paper explores several algorithms such as ACO, PSO, GA, and FA.

Studies in Computational Intelligence, 2021

This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examples included in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.