James Kennedy (born November 5, 1950) is an American social psychologist, best known as an originator and researcher of particle swarm optimization. The first papers on the topic, by Kennedy and Russell C. Eberhart, were presented in 1995; since then tens of thousands of papers have been published on particle swarms. The Academic Press / Morgan Kaufmann book, Swarm Intelligence, by Kennedy and Eberhart with Yuhui Shi, was published in 2001.
The particle swarm paradigm draws on social-psychological simulation research in which Kennedy had participated at the University of North Carolina, integrated with evolutionary computation methods that Eberhart had been working with in the 1990s. The result was a problem-solving or optimization algorithm based on the principles of human social interaction. Individuals begin the program with random guesses at the problem solution. As the program runs, the "particles" share their successes with their topological neighbors; each particle is both teacher and learner. Over time, the population converges reliably on optimal vectors.
While there has been a trend in the research literature toward a "Gbest" or centralized particle network, Blackwell and Kennedy (2018) demonstrated the importance of a distributed population topology in solving more complex problems.
A recent paper discusses the possible contribution of human female orgasm to the species' prosociality.
Kennedy has been an active combatant in the controversy over sex education in Montgomery County, Maryland, supporting the public schools' efforts to develop a comprehensive and inclusive program. He also worked to support a gender identity nondiscrimination law in Montgomery County that came under attack from conservatives, and has maintained an online progressive presence.
He also worked as a professional musician for fifty years and currently plays in a rockabilly band called The Colliders, which released albums in 2011 and 2015. In 2018 Kennedy released a DIY album, The Life of Mischief, and is currently organizing live performance of that material.
Kennedy worked in survey methods for the US government, and has conducted basic and applied research into social effects on cognition and attitude. He served as Director of the Office of Analysis and Research Services at the US International Trade Commission until his retirement in 2017. He has worked with particle swarms since 1994, with research publications in fields related and unrelated to swarms and surveys.
Kennedy, J., and Eberhart, R. C. (1999). "The particle swarm: Social adaptation in information-processing systems", in D. Corne, M. Dorigo, and F. Glover (eds.), New Ideas in Optimization, London: McGraw-Hill.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc.
In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions. Evolution of the population then takes place after the repeated application of the above operators.
In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.
In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formula over the particle's position and velocity. Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.
In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial ants stand for multi-agent methods inspired by the behavior of real ants. The pheromone-based communication of biological ants is often the predominant paradigm used. Combinations of artificial ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.g., vehicle routing and internet routing.
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.
In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic that may provide a sufficiently good solution to an optimization problem or a machine learning 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.
Marco Dorigo is a research director for the Belgian Funds for Scientific Research and a co-director of IRIDIA, the artificial intelligence lab of the Université Libre de Bruxelles. He received a PhD in System and Information Engineering in 1992 from the Polytechnic University of Milan with a thesis titled Optimization, learning, and natural algorithms. He is the leading proponent of the ant colony optimization metaheuristic, and one of the founders of the swarm intelligence research field. Recently he got involved with research in swarm robotics: he is the coordinator of Swarm-bots: Swarms of self-assembling artifacts and of Swarmanoid: Towards humanoid robotic swarms, two swarm robotics projects funded by the Future and Emerging Technologies Program of the European Commission. He is also the founding editor and editor in chief of Swarm Intelligence, the principal peer-reviewed publication dedicated to reporting research and new developments in this multidisciplinary field.
Physicomimetics is physics-based swarm (computational) intelligence. The word is derived from physike and mimesis.
Russell C. Eberhart, an American electrical engineer, best known as the co-developer of particle swarm optimization concept. He is professor of Electrical and Computer Engineering, and adjunct professor of Biomedical Engineering at the Purdue School of Engineering and Technology, Indiana University Purdue University Indianapolis (IUPUI). Fellow of the IEEE. Fellow of the American Institute for Medical and Biological Engineering.
Design Automation usually refers to electronic design automation, or Design Automation which is a Product Configurator. Extending Computer-Aided Design (CAD), automated design and Computer-Automated Design (CAutoD) are more concerned with a broader range of applications, such as automotive engineering, civil engineering, composite material design, control engineering, dynamic system identification and optimization, financial systems, industrial equipment, mechatronic systems, steel construction, structural optimisation, and the invention of novel systems.
In numerical optimization, meta-optimization is the use of one optimization method to tune another optimization method. Meta-optimization is reported to have been used as early as in the late 1970s by Mercer and Sampson for finding optimal parameter settings of a genetic algorithm.
In applied mathematics, multimodal optimization deals with optimization tasks that involve finding all or most of the multiple solutions of a problem, as opposed to a single best solution. Evolutionary multimodal optimization is a branch of evolutionary computation, which is closely related to machine learning. Wong provides a short survey, wherein the chapter of Shir and the book of Preuss cover the topic in more detail.
Multi-swarm optimization is a variant of particle swarm optimization (PSO) based on the use of multiple sub-swarms instead of one (standard) swarm. The general approach in multi-swarm optimization is that each sub-swarm focuses on a specific region while a specific diversification method decides where and when to launch the sub-swarms. The multi-swarm framework is especially fitted for the optimization on multi-modal problems, where multiple (local) optima exist.
Yuhui Shi is a pioneer in particle swarm optimization algorithms, and the developer of brain storm optimization algorithms. He was an electrical engineer from Xi'an Jiaotong-Liverpool University in Suzhou, China, where he was named a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2016 for his contributions to particle swarm optimization algorithms. He earned his PhD in electrical engineering from Southeast University, Nanjing, China in 1992, and was trained as a Post Doc Fellow at Concordia University under Canadian International Development Agency joint doctoral program, initiated by Prof. Jeremiah F. Hayes et al. He organized the first IEEE Symposium on Swarm Intelligence in 2003, and established the IEEE CIS Task Force on Swarm Intelligence in 2002, when he co-authored a book with James Kennedy and Russell C. Eberhart. He is a Chair Professor in the Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China, where he invited Prof. Jun (Steed) Huang from Joint Institutes of Carleton University and University of Ottawa collaborated on swarm intelligence robotics.
Memetic computing is a novel computational paradigm that incorporates the notion of meme(s) as basic units of transferable information encoded in computational representations for boosting the performance of artificial evolutionary systems in the domain of search and optimization.
Carlos A. Coello Coello is a Mexican computer scientist, Professor at the UNSW School of Engineering and Information Technology, and researcher at the CINVESTAV. His paper "Evolutionary algorithms for solving multi-objective problems" has been cited over 7,800 times. He won the IEEE Kiyo Tomiyasu Award in 2013.
This is a chronological table of metaheuristic algorithms that only contains fundamental algorithms. Hybrid algorithms and multi-objective algorithms are not listed in the table below.
Maurice Clerc is a French mathematician.
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