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Firefly Algorithm
In mathematical optimization, the firefly algorithm is a metaheuristic proposed by Xin-She Yang and inspired by the flashing behavior of fireflies. Algorithm In pseudocode the algorithm can be stated as: Begin 1) Objective function: 2) Generate an initial population of fireflies 3) Formulate light intensity so that it is associated with (for example, for maximization problems, 4) Define absorption coefficient while (t < MaxGeneration) for i = 1 : n (all n fireflies) for j = 1 : i (n fireflies) Vary attractiveness with distance r via move firefly i towards j; Evaluate new solutions and update light intensity; end if end for j end for i Rank fireflies and find the current best; end while end Note that the number of objective function evaluations per loop is one evalu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mathematical Optimization
Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems of sorts arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries. In the more general approach, an optimization problem consists of maxima and minima, maximizing or minimizing a Function of a real variable, real function by systematically choosing Argument of a function, input values from within an allowed set and computing the Value (mathematics), value of the function. The generalization of optimization theory and techniques to other formulations constitutes a large area of applied mathematics. More generally, opti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Metaheuristic
In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored. Metaheuristics may make relatively few assumptions about the optimization problem being solved and so may be usable for a variety of problems. Compared to optimization algorithms and iterative methods, metaheuristics do not guarantee that a globally optimal solution can be found on some class of problems. Many metaheuristics implement some form of stochastic optimization, so that the solution found is dependent on the set of random variables generated. In combinatorial optimization, by searching over a large set of feas ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Xin-She Yang
Xin-She Yang is Reader at the Middlesex University and was a senior research scientist at National Physical Laboratory, best known as a developer of various heuristic algorithms for engineering optimization. He obtained a DPhil in applied mathematics from Oxford University. He has given invited keynote talks at SEA2011, SCET2012, BIOMA2012 and Mendel Conference on Soft Computing (Mendel 2012). He has been elected as a Fellow of the Institute of Mathematics and its Application in 2021. He has been on the prestigious list of Highly Cited Researchers since 2016 by Clarivate Analyatics/Web of Science. Algorithms He created the firefly algorithm (2008), cuckoo search (2009), bat algorithm (2010),Yang X.-S., A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) (Eds. J. R. Gonzalez et al.), Studies in Computational Intelligence, Springer Berlin, 284, Springer, 65–74 (2010) and flower pollination algorithm (2012). ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Firefly
The Lampyridae are a family of elateroid beetles with more than 2,000 described species, many of which are light-emitting. They are soft-bodied beetles commonly called fireflies, lightning bugs, or glowworms for their conspicuous production of light, mainly during twilight, to attract mates. Light production in the Lampyridae is thought to have originated as an honest warning signal that the larvae were distasteful; this was co-opted in evolution as a mating signal in the adults. In a further development, female fireflies of the genus ''Photuris'' mimic the flash pattern of ''Photinus'' species to trap their males as prey. Fireflies are found in temperate and tropical climates. Many live in marshes or in wet, wooded areas where their larvae have abundant sources of food. While all known fireflies glow as larvae, only some species produce light in their adult stage, and the location of the light organ varies among species and between sexes of the same species. Fireflies ha ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
MATLAB
MATLAB (an abbreviation of "MATrix LABoratory") is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages. Although MATLAB is intended primarily for numeric computing, an optional toolbox uses the MuPAD symbolic engine allowing access to symbolic computing abilities. An additional package, Simulink, adds graphical multi-domain simulation and model-based design for dynamic and embedded systems. As of 2020, MATLAB has more than 4 million users worldwide. They come from various backgrounds of engineering, science, and economics. History Origins MATLAB was invented by mathematician and computer programmer Cleve Moler. The idea for MATLAB was based on his 1960s PhD thesis. Moler became a math professor at the University of New Mexico and starte ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Metaheuristic
In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored. Metaheuristics may make relatively few assumptions about the optimization problem being solved and so may be usable for a variety of problems. Compared to optimization algorithms and iterative methods, metaheuristics do not guarantee that a globally optimal solution can be found on some class of problems. Many metaheuristics implement some form of stochastic optimization, so that the solution found is dependent on the set of random variables generated. In combinatorial optimization, by searching over a large set of feas ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
List Of Metaphor-inspired Metaheuristics
This is a chronologically ordered list of metaphor-based metaheuristics and swarm intelligence algorithms, sorted by decade of proposal. Algorithms 1980s-1990s Simulated annealing (Kirkpatrick et al., 1983) Simulated annealing is a probabilistic algorithm inspired by annealing, a heat treatment method in metallurgy. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For problems where finding the precise global optimum is less important than finding an acceptable local optimum in a fixed amount of time, simulated annealing may be preferable to alternatives such as gradient descent. The analogue of the slow cooling of annealing is a slow decrease in the probability of simulated annealing accepting worse solutions as it explores the solution space. Accepting worse solutions is a fundamental property of metaheuristics because it allows for a more extensive search for the optimal solution. Ant colony optimization (ACO) ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Genetic And Evolutionary Computation Conference
The Genetic and Evolutionary Computation Conference (GECCO) is the premier conference in the area of genetic and evolutionary computation. GECCO has been held every year since 1999, when it was first established as a recombination of the International Conference on Genetic Algorithms (ICGA) and the Annual Genetic Programming Conference (GP). GECCO presents the latest high-quality results in genetic and evolutionary computation. Topics of interest include: genetic algorithms, genetic programming, evolution strategies, evolutionary programming, estimation of distribution algorithms, memetic algorithms, hyper-heuristics, evolutionary robotics, evolvable hardware, artificial life, ant colony optimization algorithms, swarm intelligence, artificial immune systems, digital entertainment technologies, evolutionary art, evolutionary combinatorial optimization, metaheuristics, evolutionary multi-objective optimization, evolutionary machine learning, search-based software engineering ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Swarm Intelligence
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. SI systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment.Hu, J.; Turgut, A.; Krajnik, T.; Lennox, B.; Arvin, F.,Occlusion-Based Coordination Protocol Design for Autonomous Robotic Shepherding Tasks IEEE Transactions on Cognitive and Developmental Systems, 2020. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
