PSO and ACO in Optimization Problems

Memetic Computing, 2011

This special issue collects a selection of the best papers emerging from the 2007 International Workshop on Nature inspired cooperative strategies for optimisation (NICSO 2007). Nature inspired strategies are being used in ever more widely ranging settings spanning not only computational problem solving (e.g. Neural Networks, Simulated Annealing, Ant Colony Optimisation, etc.) but also on a variety of other disciplines such as chemistry, physics and engineering. Moreover, cooperative strategies are gaining momentum across many computer science disciplines such as machine learning, classification, data mining, and, of course, optimisation.

IEEE Transactions on Evolutionary Computation, 2000

Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a selforganized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population.

2014

International Journal of Digital Contents and Applications for Smart Devices, 2014

Human being are greatly inspired by nature. Nature has the ability to solve very complex problems in its own distinctive way. The problems around us are becoming more and more complex in the real time and at the same instance our mother nature is guiding us to solve these natural problems. Nature gives some of the logical and effective ways to find solution to these problems. Nature acts as an optimizer for solving the complex problems. In this paper, the algorithms which are discussed imitate the processes running in nature. And due to this these process are named as "Nature Inspired Algorithms". The algorithms inspired from human body and its working and the algorithms inspired from the working of groups of social agents like ants, bees, and insects are the two classes of solving such Problems. This emerging new era is highly unexplored young for the research. This paper proposes the high scope for the development of new, better and efficient techniques and application in this area.

2006

Biological and natural processes have been influencing the methodologies in science and technology since a long time ago. The work of Wiener in cybernetics was influenced by feedback control processes observable in biological systems; McCulloch and Pitts' description of the artificial neuron was seeded from mathematical biology and electronics; the idea of "survival of the fittest" inspired the whole field of genetic algorithms and nowadays, the natural immune system is being considered as a source of inspiration for networks security.

Biological processes and methods have been influencing science and technology for many decades. The ideas of feedback and control processes Norbert Wiener used in his cybernetics were based on observation of these phenomena in biological systems. Artificial intelligence and intelligent systems have been fundamentally interested in the phenomenology of living systems, namely perception, decision-making, action, and learning. Natural systems exhibit many properties that form fundamentals for a number of nature inspired applications -dynamics, flexibility, robustness, self-organisation, simplicity of basic elements, and decentralization. This paper reviews examples of nature inspired software applications, mostly drawing inspiration from collective behaviour of social colonies.

Natural Computing, 2010

This special issue collects a selection of the best papers emerging from the 2007 International Workshop on Nature inspired cooperative strategies for optimisation (NICSO 2007). Nature inspired strategies are being used in ever more widely ranging settings spanning not only computational problem solving (e.g. Neural Networks, Simulated Annealing, Ant Colony Optimisation, etc.) but also on a variety of other disciplines such as chemistry, physics and engineering. Moreover, cooperative strategies are gaining momentum across many computer science disciplines such as machine learning, classification, data mining, and, of course, optimisation.

Nature has provided rich models for computational problem solving, including optimizations based on the swarm intelligence exhibited by fireflies, bats, and ants. These models can stimulate computer scientists to think nontraditionally in creating tools to address application design challenges. Most if not all engineering tasks involve decisions about the product, service, and system design, which are related in some way to optimizing time and resources as well achieving balance between maximizing performance, profit, sustainability, quality, safety, and efficiency and minimizing cost, energy consumption, defects, and environmental impact. As the sidebar " Elements of an Optimization Problem " describes, many of these design problems have multiple objectives bound by highly complex constraints. The traditional approach of specializing design method to problem type does not fit well with complex, nonlinear problems, such as multicriteria engineering designs and multiple complex feature extraction in big data. This lack of suitability has motivated interest in more novel optimization approaches. One emerging trend is to combine heuristic search with multiagent systems to solve real-world business and engineering problems. 1 Such a combination has accuracy, efficiency, and performance advantages over specialized methods. For example, it can be used to more efficiently and accurately optimize or tune parameters in artificial neural networks, which are essential to many AI tasks. One class of novel optimization algorithms is based on swarm intelligence (SI). SI captures the idea that decision making among organisms in a community, such as ants and bees, uses local information and interactions with other agents and with their own environment, which in turn could be responsible for the rise of collective or social intelligence. One hypothesis is that complex interactions directly or indirectly contribute to the emergence of intelligence in highly developed biological species. The reasoning is that biological change results from the organism responding and adapting to alterations in its community and environment. Groups of

2007

Social insects-ants, bees, wasps and termites-and the distributed problem-solving, multiagent paradigm that they represent have been enormously influential in nature inspired computing. Insect societies have been a source of inspiration and amazement for centuries but only in the last 25 years or so have we made significant inroads to both understanding just how various collective phenomena arise and are governed, and how we can use the lessons and insights garnered from sociobiological research for more practical purposes. In this chapter, we provide a very brief history of the field, detailing some of the key phenomena, mechanisms and lessons learned and a quick tour of some of the different types of applications to which this knowledge has been put to use, including but certainly not limited to distributed problem solving, task allocation, search and collective robotics.

2019

Bio-Inspired optimization algorithms are inspired from principles of natural biological evolution and distributed collective of a living organism such as (insects, animal, …. etc.) for obtaining the optimal possible solutions for hard and complex optimization problems. In computer science Bio-Inspired optimization algorithms have been broadly used because of their exhibits extremely diverse, robust, dynamic, complex and fascinating phenomenon as compared to other existing classical techniques. This paper presents an overview study on the taxonomy of bio-inspired optimization algorithms according to the biological field that are inspired from and the areas where these algorithms have been successfully applied