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双语推荐：算法

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现代最优化算法比较常见的有遗传算法、蚁群算法、粒子群算法、鱼群算法和模拟退火算法。这些算法主要是解决优化问题中的难解问题。文章主要是对遗传算法、粒子群算法和模拟退火算法三个算法的优化性能进行比较。首先介绍了三个算法的基本思想,以此可以了解三种算法有着自身的特点和优势,而后用这三种算法对典型函数进行计算,并对优化结果比较分析,提出了今后研究的方向。
Modern optimization includes genetic algorithm (GA), ant colony algorithm (ACO), particle swarm algorithm optimization (PSO), fish-swarm algorithm and simulated annealing algorithm (SA) and so on. They are mainly applied to solve some difficult optimization problems. The paper mainly makes a comparative study of the optimization performance of GA, PSO and SA. First the basic principles of the three algorithms are introduced, and the characteristics and advantages of these algorithms are understood. At last, the three algorithms are used for typical functions calculation, and comparative analysis is made to the results. And the future research directions are put forward.

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用于解决最短路径问题的算法被称做最短路径算法。最短路径算法在各种应用中有着广泛的用途。常用的路径算法有Dijkstra算法、Bellman—Ford算法、SPFA算法和DAG图算法，本文对这些算法进行了分析比较。
The shortest path algorithm is the algorithm to solve the shortest path problem. The shortest path algorithm has been widely used in a variety of applications. Dijkstra algorithm, Bellman-Ford algo-rithm, SPFA algorithm and DAG graph algorithms are common shortest path algorithms. This paper analyzes and compares these algorithms.

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通常元启发式优化算法是基于群体智能的算法，这些算法通常被称为智能算法。此文综述一些广泛应用的优化算法，包括蚁群算法、蜜蜂算法、蝙蝠算法、布谷鸟搜索、萤火虫算法和粒子群优化。同时，还讨论启发式算法中具有挑战性的问题如参数调整和参数控制。
Metaheuristic optimization algorithms are usually based on swarm intelligence ,and these algorithms are often referred to as smart algorithms .We review some of the widely used algorithms for optimization ,including ant and bee algorithms ,bat algorithm ,cuckoo search , firefly algorithm and particle swarm optimization .We also discuss the challenging issues con-cerning parameter tuning and parameter control in metaheuristic algorithms .

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研究了低能见度条件下的图像清晰化方法,在基于频率域与空间域的传统算法的基础上,提出了传统算法互相结合的新算法,包括POSHE算法、基于POSHE算法与传统算法相结合的算法、有限对比自适应直方图均衡化的算法、基于CLAHE算法和POSHE算法与传统算法相结合的算法。以上几种算法,都能对含沙尘的这类图像进行不同程度的增强,得到了较为满意的结果。
@@@@The paper was researched methods of image sharpening in condition of low visibility. Based on traditional algo-rithm of frequency domain and space domain, the paper was put forward new algorithm combined traditional algorithm, in-cluding POSHE algorithm, combining traditional algorithm based on POSHE, limited comparison adaptive histogram equaliza-tion algorithm and combining traditional algorithm based on CLAHE algorithm and POSHE algorithm. Above algorithms strengthened the kind of dust image in different degree and obtained approving results.

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烟花算法由于具有很强的优化问题求解的能力,近年来逐渐受到研究者的广泛关注。对现有烟花算法的研究工作进行了全面总结,主要包括烟花算法提出的背景、烟花算法的基本原理、单目标烟花算法的改进、混合算法、多目标烟花算法、基于GPU的并行烟花算法以及烟花算法在实际问题中的应用研究等。对于单目标烟花算法及改进算法、混合算法,文中给出了各种改进烟花算法的机制分析和对比研究,最后,给出了烟花算法的未来研究方向,包括爆炸算子搜索机制的深入分析、烟花交互机制研究、多目标烟花算法研究、并行烟花算法研究、扩展烟花算法求解的问题类型以及应用拓展。
Fireworks algorithm ( FWA ) has shown great successes in dealing with complex optimization problems and has attracted a great amount of attention recently.In this paper, FWA was completely analyzed, Including the FWA background evaluation, the study of fundamental principles of FWA, developments in single objective FWA optimization, hybrid algorithms, multi-objective fireworks algorithm, graphic processing unit( GPU) based parallel fireworks algorithm, and their applications in practice.For single objective FWA and improved and hybrid algo-rithms, the mechanism analysis and comparative research of various improved FWAs are given in this paper.Final-ly, the future research directions for FWA are pointed out, which include the analysis of explosion operator, study of interaction strategies among the fireworks, research on multi-objective fireworks algorithm and parallel fireworks algorithm, types of solutions to the extended FWA, and application expansion.

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生物地理学优化算法（Biogeography-Based Optimization，BBO）是Simon提出的一种基于生物地理学理论的新型智能优化算法，具有良好的收敛性和稳定性。从BBO算法提出的背景出发，介绍了算法的基本理论、算法特点以及算法流程。总结了BBO算法的研究进展，包括BBO算法的理论分析、算法的改进、算法与其他优化算法的混合算法以及BBO算法在函数优化、电力系统、图像处理、机器人路径规划以及调度优化等领域的典型应用。对BBO算法有待解决的问题和未来研究方向进行了总结。
Simon Dan proposes a new type of intelligence optimization algorithm based on the biogeography theory, named the Biogeography-Based Optimization algorithm(BBO), and it has good ability of convergence and stability. From the proposed background of the BBO algorithm, the basic theory, characteristics and the steps of the BBO algorithm are discussed. The research progress is summarized, including the theory analysis, the improvement of BBO algorithm, and the hybrid algorithm to other optimization algorithms. And several typical application areas of BBO algorithm are surveyed respec-tively including function optimization, power system, image processing, robot path planning, and scheduling optimization. The problems to be solved and future research directions of BBO algorithm are summarized.

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旅行售货商问题(简称TSP )是离散优化的一个经典的重要问题，对求解算法的研究非常重要。在介绍求解TSP问题的贪婪算法、禁忌搜索算法、模拟退火算法、遗传算法的基本思想之后，提出了相应的算法。针对测试库的四个典型算例，用程序实现这些算法，对这些算法的运行时间和结果进行比较研究。结果表明贪婪算法短时间就可以得出解，禁忌搜索算法与遗传算法的效果相当，模拟退火算法比遗传算法的结果好。
The traveling salesman problem (TSP) is an important problem for the classical discrete optimization, which is very important to study the solving algorithm. After the introduction of the greedy algorithm, taboo search algorithm, simulated annealing algorithm, genetic algorithm, the author put forward the corresponding algorithm. Aiming at the four typical examples in the test base, we realized implementation of these algorithms with procedures, and the running time and the results of these algorithms are compared. The results show that the greedy algorithm can draw the solution in a short time, the taboo search algorithm and genetic algorithm have the same effect, and the results of simulated annealing algorithm is better than those of genetic algorithm.

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隐马尔可夫模型（Hidden Markov Model,HMM）的基本算法体系主要包括Baum-Welch算法、前向-后向算法与Viterbi算法三大经典算法,通过展开对HMM新问题及新算法的理论研究,引出逆维特比问题及逆维特比算法的理论体系,并提出将逆维特比算法引入HMM基本算法体系中构建一种新的算法体系及一种新评估体系的构想,最后对新算法体系的应用进行了展望。
The system of the basic Hidden Markov Model (HMM) algorithm includes the three important algorithms of Baum-Welch algorithm,Forward-Backward algorithm and Viterbi algorithm. The Inv-viterbi problem and its corresponding algorithm is put forward in the paper after the research of the new HMM problem and algorithm. Then a new algorithm system and a new evaluation system are built by introducing the Inv-viterbi algorithm into the HMM basic algorithm system. Finally we give a prospect on the application of the new algorithm system.

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针对传统遗传算法和粒子群算法在实际应用中存在早熟和收敛效率低的问题,引入了遗传算法和粒子群算法的混合算法。在混合算法中引入种群分割策略,避免了算法陷入局部最优解;引入粒子群重构变异算子避免了算法的早熟,并提高了算法的收敛效率。针对系统效益和时间开销等性能指标进行了仿真,仿真结果表明,改进算法与传统遗传算法和粒子群算法相比,能够提高搜索效率,得到更好的系统效益。
The traditional genetic algorithm and particle swarm algorithm have problems of prematurity and low convergence effi-ciency in the practical application.In order to solve the problems,an algorithm based on a hybrid of genetic algorithm and particle swarm algorithm is introduced.The population segmentation strategy is introduced to avoid algorithm trapped in local optimal solution. The particle swarm refactoring mutation operator is introduced to avoid prematurity and improve the convergence efficiency of the algorithm. Finally, performance indicators of time cost and system efficiency are simulated.The improved algorithm is compared with traditional genetic algorithm and particle swarm algorithm.Simulation results show that it performs better both in terms of convergence speed and system efficiency.

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根据蚁群算法和遗传算法收敛性互补的特点,提出了一种基于目标函数变化率的混合蚁群遗传算法。该算法的基本思想是：用蚁群算法的解作为遗传算法的初始种群,根据目标函数的变化率交叉地调用蚁群算法和遗传算法。每当种群进化接近停滞时,调用蚁群算法。这种方法可动态地控制蚁群算法和遗传算法的调用时机,再配合相应的信息素更新方法,以提高算法的收敛性。将新算法用于车间调度基准测试问题,仿真结果表明,与常规混合蚁群遗传算法相比,新算法的全局收敛性和局部收敛性有了明显的提高。
According to the complementary on convergence of ant colony algorithm and genetic algorithm, a hybrid ant colony genetic algorithm based on the change rate of objective function is put forward. The basic idea of new algorithm is that the solutions of ant colony algorithm are given as the initial population of genetic algorithm, and the ant colony algorithm and genetic algorithm are dynamically called according to the change rate of objective function. When population evolutionary is close to stagnation, ant colony algorithm is called. This approach can dynamically control the call timing of ant colony algorithm and genetic algorithm. Together with the pheromone update methods, the convergence of hybrid algorithm is improved. The new algorithm is used to solve the Job shop scheduling benchmarks problem, and the simulation results show that the global and local convergence perfor-mance of new algorithm has been significantly improved compared with basic hybrid ant colony genetic algorith

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