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首页> 外文期刊>Current Science: A Fortnightly Journal of Research >Computational algorithms inspired by biological processes and evolution
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Computational algorithms inspired by biological processes and evolution

机译：受生物过程和进化启发的计算算法

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摘要

In recent times computational algorithms inspired by biological processes and evolution are gaining much popularity for solving science and engineering problems. These algorithms are broadly classified into evolutionary computation and swarm intelligence algorithms, which are derived based on the analogy of natural evolution and biological activities. These include genetic algorithms, genetic programming, differential evolution, particle swarm optimization, ant colony optimization, artificial neural networks, etc. The algorithms being random-search techniques, use some heuristics to guide the search towards optimal solution and speed-up the convergence to obtain the global optimal solutions. The bio-inspired methods have several attractive features and advantages compared to conventional optimization solvers. They also facilitate the advantage of simulation and optimization environment simultaneously to solve hard-to-define (in simple expressions), real-world problems. These biologically inspired methods have provided novel ways of problem-solving for practical problems in traffic routing, networking, games, industry, robotics, economics, mechanical, chemical, electrical, civil, water resources and others fields. This article discusses the key features and development of bio-inspired computational algorithms, and their scope for application in science and engineering fields.
机译：近年来，受生物过程和进化启发的计算算法在解决科学和工程问题上越来越受欢迎。这些算法大致分为进化计算和群体智能算法，它们是基于自然进化和生物活动的类推而得出的。其中包括遗传算法，遗传程序设计，差分进化，粒子群优化，蚁群优化，人工神经网络等。这些算法是随机搜索技术，使用一些启发式方法来引导搜索朝着最优解的方向发展，并加快收敛速度​​。获得全局最优解。与传统的优化求解器相比，受生物启发的方法具有许多吸引人的特征和优点。它们还利用仿真和优化环境的优势，同时解决了难以定义（用简单的表达式表示）的实际问题。这些受到生物学启发的方法为交通路线，网络，游戏，工业，机器人技术，经济学，机械，化学，电气，民用，水资源等领域的实际问题提供了解决问题的新颖方法。本文讨论了生物启发式计算算法的关键特性和发展，以及它们在科学和工程领域中的应用范围。

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