A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm

Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.  

遗传算法基础理论研究的新近发展

本文综述了有关遗传算法收敛性及收敛速度估计的近期研究结果，在分类概述相关的Ｖｏｓｅ－Ｌｉｅｐｉｎｓ模型、Ｍａｒｋｏｖ链模型、公理化模型、连续（积分算子）模型及收敛速度估计、迭代次数估计与时间复杂性估计的基础上，指出遗传算法理论研究存在的其它亟待解决的问题。  

A comparative study of Artificial Bee Colony algorithm

Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm and evolution strategies. Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters.  

遗传算法BP神经网络的预报研究和应用

针对目前 BP神经网络在实际气象预报应用中 ,网络结构难以确定以及网络极易陷入局部解问题 ,用遗传算法优化神经网络的连接权和网络结构 ,并在遗传进化过程中采取保留最佳个体的方法 ,建立基于遗传算法的 BP网络模型 ,并以广西的月降水量进行实例分析 ,计算结果表明 ,该方法预报精度高、而且稳定 .  

A Genetic Algorithm for the Multidimensional Knapsack Problem

In this paper we present a heuristic based upon genetic algorithms for the multidimensional knapsack problem. A heuristic operator which utilises problem-specific knowledge is incorporated into the standard genetic algorithm approach. Computational results show that the genetic algorithm heuristic is capable of obtaining high-quality solutions for problems of various characteristics, whilst requiring only a modest amount of computational effort. Computational results also show that the genetic algorithm heuristic gives superior quality solutions to a number of other heuristics.  

A Genetic Algorithm for the Multidimensional Knapsack Problem

In this paper we present a heuristic based upon genetic algorithms for the multidimensional knapsack problem. A heuristic operator which utilises problem-specific knowledge is incorporated into the standard genetic algorithm approach. Computational results show that the genetic algorithm heuristic is capable of obtaining high-quality solutions for problems of various characteristics, whilst requiring only a modest amount of computational effort. Computational results also show that the genetic algorithm heuristic gives superior quality solutions to a number of other heuristics.  

资源公平分配的遗传算法研究

文章对决策优化的经典问题席位公平分配进行了研究，提出应用遗传算法对该模型进行求解。两例资源公平分配决策实例研究表明，遗传算法优化结果较其它方法都更合理。  

求解TSP的子空间遗传算法

为避免遗传算法在计算过程中搜索冗余空间而耗费不必要的资源及时间，本提出了一种以经费遗传算法为基础，通过分析问题的特殊解集，以找出原问题解空间的区域特征从而构造出缩小算法搜索空间的子空间遗传算法，并用它求解TSP。结果表明，该算法实施起来非常有效。  

A note on chance constrained programming with fuzzy coefficients

This paper deals with nonlinear chance constrained programming as well as multiobjective case and goal programming with fuzzy coefficients occurring in not only constraints but also objectives. We also present a fuzzy simulation technique for handling fuzzy objective constraints and fuzzy goal constraints. Finally, a fuzzy simulation based genetic algorithm is employed to solve a numerical example.  

Toward Fuzzy Optimization without Mathematical Ambiguity

Fuzzy programming has been discussed widely in literature and applied in such various disciplines as operations research, economic management, business administration, and engineering. The main purpose of this paper is to present a brief review on fuzzy programming models, and classify them into three broad classes: expected value model, chance-constrained programming and dependent-chance programming. In order to solve general fuzzy programming models, a hybrid intelligent algorithm is also documented. Finally, some related topics are discussed.