An improved binary particle swarm optimization for unit commitment problem

This paper proposes a new improved binary PSO (IBPSO) method to solve the unit commitment (UC) problem, which is integrated binary particle swarm optimization (BPSO) with lambda-iteration method. The IBPSO is improved by priority list based on the unit characteristics and heuristic search strategies to repair the spinning reserve and minimum up/down time constraints. To verify the advantages of the IBPSO method, the IBPSO is tested and compared to the other methods on the systems with the number of units in the range of 10–100. Numerical results demonstrate that the IBPSO is superior to other methods reported in the literature in terms of lower production cost and shorter computational time.     

改进粒子群算法在高维复杂函数寻优中的应用

针对粒子群算法应用于复杂函数优化时可能出现过早收敛于局部最优解的情况,提出了一种改进的算法结构,命名为多阶段多子群粒子群算法（Multi-stage Multi-subpopulation Particle Swarm Optimization,MMPSO）,该方法主要通过多子群之间阶段性的重分组策略,强化不同群体之间的信息交流,增大其搜索到全局最优解的概率,同时,为了保留粒子群算法高效优化的特点,通过分阶段搜索模式的转变,将全局最好模型收敛的快速性和局部最好模型收敛的全局最优性进行折中,确保改进后的粒子群算法拥有更强的全局搜索能力和尽量高的收敛速度。仿真实验证明,MMPSO算法相对于基本粒子群算法而言具有明显的精度优势。     

复形法粒子群优化算法研究

针对基本粒子群优化算法对复杂函数优化时难以获得最优解的缺陷,提出了一种复形粒子群优化算法。该算法采用复形法来提高粒子的局部搜索能力,从而保证了算法能够跳出局部最优,获得全局最优解。实验结果表明,与文献算法相比,该算法在基准函数优化时具有更强的寻优能力和更高的搜索精度。     

Neutrino-like particle for particle swarm optimization

The real-world optimal problems frequently encountered by various industries are the nonlinear constrained optimization problems (NCOPs), where the constraints represent the limitations of practical resources. Many researchers have attempted to improve particle swarm optimization (PSO) in the past decades; however, in solving the NCOPs, the PSO-based approaches often cause premature convergences. The problem-specific constraints frequently generate many infeasible regions that block the movements of particles. The particles' behavior causes the exploration abilities of particles that tend to weaken along with time. The decreasing of exploration ability often comes from the particle becoming stagnant or moving unusefully. This study proposes a neutrino-like particle (NLP) with adaptive NLP hyperparameters that simulate the natural neutrino behavior. The proposed NLPs can be embedded in the PSO-based approaches for overcoming premature convergence. The experiment results demonstrate that all referenced PSO-based methods with the NLPs improved significantly compared with those without the NLPs to solve the NCOPs. All referenced PSO-based methods that embedded the NLPs also significantly outperform four recent strong algorithms in most IEEE CEC 2020 benchmark problems. Therefore, the proposed NLPs with adaptive NLP hyperparameters can effectively solve the premature convergences, reinforce the exploration ability, and maintain the exploitation capability for solving the NCOPs over the whole evolution process.     

结合灰色理论和粒子群算法的归一化图像分割*

为了对多目标图像进行有效分割,使用灰色关联分析来衡量像素点间的相似性,并将normalized cut准则推导为双闽值分割准则,最后使用粒子群算法优化该准则,得出最佳的分割阈值.实验表明该方法计算速度快,能有效分割多目标图像.     

电力系统机组组合问题的动态双种群粒子群算法

针对标准粒子群优化算法易陷入局部最优点的缺点,提出了动态双种群粒子群优化算法（DDPSO）。该算法中两个子种群规模随进化过程不断变化,进化中分别采用不同的学习策略且相互交换信息。将该算法应用于机组组合问题中,采用实数矩阵编码方法对发电计划进行编码,将两层优化问题转化为单层优化问题,直接运用DDPSO算法求解。仿真结果表明,用该方法解决机组组合问题具有良好的精度和鲁棒性。     

A study of graphics hardware accelerated particle swarm optimization with digital pheromones

Programmable Graphics Processing Units (GPUs) have lately become a promising means to perform scientific computations. Modern GPUs have proven to outperform the number of floating point operations when compared to traditional Central Processing Units (CPUs) through inherent data parallel architecture and higher bandwidth capabilities. They allow scientific computations to be performed without noticeable degradation in accuracy in a fraction of the time compared to traditional CPUs at substantially reduced costs, making them viable alternatives to expensive computer clusters or workstations. GPU programmability however, has fostered the development of a variety of programming languages making it challenging to select a computing language and use it consistently without the pitfall of being obsolete. Some GPU languages are hardware specific and are designed to rake in performance boosts when used with their host GPUs (e.g., Nvidia Cuda). Others are operating system specific (e.g., Microsoft HLSL). A few are platform agnostic lending themselves to be used on a workstation with any CPU and a GPU (e.g., GLSL, OpenCL).Of a number of companies and organizations that implement formal optimization into their processes, only a few utilize GPUs. It is either because the others are either vested much into CPU based computing or they are not fully aware of the benefits of implementing population based optimization routines in GPUs. Literature shows a large number of research publications specifically in the field of optimization utilizing GPUs. However, most of them are limited to a specific GPU hardware or addressed specific problems. The diversity in current GPU hardware and software APIs present overwhelming number of choices making it challenging to decide where and how to begin transitioning to GPU based computing, impeding promising computing avenues that relatively is very cost effective. In this paper, the authors precisely intend to address some of these issues by broadly classifying GPU APIs into three categories: 1) Hardware vendor dependent GPU APIs, 2) Graphical in context APIs, and 3) Platform agnostic APIs. Prior work by the authors demonstrated the capability of digital pheromones within Particle Swarm Optimization (PSO) for searching n-dimensional design spaces with improved accuracy, efficiency and reliability in serial and parallel CPU computing environments. To study the impact of GPUs, the authors have taken this digital pheromone variant of PSO and implemented it on three GPU APIs, each representing a category listed above, in a simplistic sense – delegate unconstrained explicit objective function evaluations to GPUs. While this approach itself cannot be considered novel, the takeaways from implementing it on different GPU APIs provided a wealth of information that the authors believe can help optimization companies and organizations make informed decisions in implementing GPUs in their processes.     

Non-parametric particle swarm optimization for global optimization

In recent years, particle swarm optimization (PSO) has extensively applied in various optimization problems because of its simple structure. Although the PSO may find local optima or exhibit slow convergence speed when solving complex multimodal problems. Also, the algorithm requires setting several parameters, and tuning the parameters is a challenging for some optimization problems. To address these issues, an improved PSO scheme is proposed in this study. The algorithm, called non-parametric particle swarm optimization (NP-PSO) enhances the global exploration and the local exploitation in PSO without tuning any algorithmic parameter. NP-PSO combines local and global topologies with two quadratic interpolation operations to increase the search ability. Nineteen (19) unimodal and multimodal nonlinear benchmark functions are selected to compare the performance of NP-PSO with several well-known PSO algorithms. The experimental results showed that the proposed method considerably enhances the efficiency of PSO algorithm in terms of solution accuracy, convergence speed, global optimality, and algorithm reliability.     

基于改进粒子群优化的胶粘剂生产过程温度控制

针对大惯性、纯迟延、非线性、时变的胶粘剂生产过程,提出一种改进粒子群优化的PID控制算法。该算法针对常规PID设计方法存在的缺点,提出了一种可兼顾多项性能指标的PID控制器参数整定的改进粒子群优化方法。该方法将遗传算法中的变异思想引入到标准的粒子群优化算法中,避免了算法陷入局部极值点,以寻优PID控制器参数。将该方法应用于胶粘剂生产过程,较好地实现了反应釜温度的跟踪控制。仿真结果和实际情况表明所提出算法的有效性和优越性。     

基于遗传粒子群算法的高维复杂函数优化方法

针对高维复杂函数优化的特点,提出了一种遗传算法与粒子群算法相结合的主-从结构算法。算法中,主级为全局搜索的遗传算法;从级为局部邻域搜索的粒子群算法。通过主-从协调机制和从级转换函数设计,使算法不依赖复杂的编码方式和进化算子进行全局精确搜索。通过仿真和比较实验,验证了算法对高维复杂函数优化的有效性。     

双种群变异粒子群算法

利用变异机制可以增加遗传算法全局寻优能力的特性,结合惯性权值线性递减PSO算法具有较快收敛速度的优点,提出了一种双种群变异PSO算法,对该算法与其他PSO算法进行了比较,仿真结果表明其性能优越。     

基于混合粒子群算法的高维优化问题求解

为解决高维复杂函数的优化问题，克服标准粒子群算法早熟收敛、局部搜索能力弱等缺点，在标准粒子群优化算法中融合了遗传算法的设计思想，提出了一种新颖的混合粒子群算法。高维函数个别维上的差解导致算法最终无法找到全局最优解，而通常的优化算法很难寻找到每一维上的最佳值。受遗传算法思想的启发，在粒子的进化过程中，通过对最优粒子的每一维进行评价，找到导致最终解质量差的维度，对其维上的数据进行变异，进而有针对性地改进，寻找到每一维上的最佳位置。对典型高维复杂函数的仿真表明：算法在求解质量和求解速度两方面都得到了好的结果。     

图形处理器光照引擎的设计

以计算机图形学为基础,OpenGL API为设计规约,设计的三维图形处理器,具有较好的通用性。重点剖析了图形处理器光照引擎事务级模型的设计,该事务级模型采用SystemC编写,在VC++6.0下完成开发调试工作,为寄存器传输级的光照引擎IP软核开发奠定了良好的基础。     

求解约束优化的改进粒子群算法

针对种群初始化时粒子过于集中和基本粒子群算法搜索精度不高的缺陷,提出了一种求解约束优化问题的改进粒子群算法。该算法引入佳点集技术来优化种群的初始粒子,使种群粒子初始化时分布均匀,因而种群具有多样性,不会陷入局部极值;同时使用协同进化技术使双种群之间保持通信,从而提高算法的搜索精度。仿真实验结果表明：将该算法用于5个基准测试函数,该算法均获得了理论最优解,其中有4个函数的测试方差为0。该算法提高了计算精度且鲁棒性强,可以广泛应用于其他约束优化问题中。     

改进的量子粒子群多目标优化算法

针对粒子群优化算法容易陷入局部极值点的问题,提出了一种新的量子比特粒子群算法,该算法采用Pareto支配关系来更新粒子的个体最优值和局部最优值;定义极大极小距离,并采用该距离方法裁减非支配解.实验结果表明该算法能更好地接近Pareto前沿且具有更好的分布性,更适合于求解复杂高维优化问题,是一种非常有潜力的多目标优化方法.     

Example-based learning particle swarm optimization for continuous optimization

Particle swarm optimization (PSO) is a heuristic optimization technique based on swarm intelligence that is inspired by the behavior of bird flocking. The canonical PSO has the disadvantage of premature convergence. Several improved PSO versions do well in keeping the diversity of the particles during the searching process, but at the expense of rapid convergence. This paper proposes an example-based learning PSO (ELPSO) to overcome these shortcomings by keeping a balance between swarm diversity and convergence speed. Inspired by a social phenomenon that multiple good examples can guide a crowd towards making progress, ELPSO uses an example set of multiple global best particles to update the positions of the particles. In this study, the particles of the example set were selected from the best particles and updated by the better particles in the first-in-first-out order in each iteration. The particles in the example set are different, and are usually of high quality in terms of the target optimization function. ELPSO has better diversity and convergence speed than single-gbest and non-gbest PSO algorithms, which is proved by mathematical and numerical results. Finally, computational experiments on benchmark problems show that ELPSO outperforms all of the tested PSO algorithms in terms of both solution quality and convergence time.     

Cellular particle swarm optimization

This paper proposes a cellular particle swarm optimization (CPSO), hybridizing cellular automata (CA) and particle swarm optimization (PSO) for function optimization. In the proposed CPSO, a mechanism of CA is integrated in the velocity update to modify the trajectories of particles to avoid being trapped in the local optimum. With two different ways of integration of CA and PSO, two versions of CPSO, i.e. CPSO-inner and CPSO-outer, have been discussed. For the former, we devised three typical lattice structures of CA used as neighborhood, enabling particles to interact inside the swarm; and for the latter, a novel CA strategy based on “smart-cell” is designed, and particles employ the information from outside the swarm. Theoretical studies are made to analyze the convergence of CPSO, and numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on benchmark test functions.     

多目标微粒群优化算法

通过设计一种Pareto解集过滤器,并在此基础上给出多目标优化条件下的微粒群算法群体停滞判断准则,基于该准则提出了一种多目标微粒群优化算法。算法利用Pareto解集过滤器提高了候选解的多样性,并使用图形法将所提算法与经典的多目标优化进化算法在一组标准测试函数上进行了比较,结果表明算法具有更好的搜索效率。     

协同粒子群优化算法

为解决粒子群优化算法易陷入局部最优的问题,提出了两种新方法协同处理粒子群优化算法：对比平均适应度值差的粒子,用动态Zaslavskii混沌映射公式改进粒子惯性权重与速度矢量,在复杂多变的环境中逐步摆脱局部最优值,动态寻找全局最优值;对好于或等于适应度平均值的粒子,用动态非线性函数调整粒子惯性权重与速度矢量,在保存相对有利环境的基础上逐步向全局最优处收敛。两种方法相辅相成、动态协调,使两个动态种群相互协作、协同进化。实验表明该算法在多个标准测试函数下都超越了同类著名改进算法。     

改进的粒子群算法

为改善基本粒子群算法的搜索性能,针对粒子群算法随机性较强、收敛较慢的问题,利用数学中的外推技巧给出了两个新的粒子位置更新公式,由此构造出一种新的算法--强引导型粒子群算法.新算法对粒子位置更新加以引导,试图减少算法的随机性以提高搜索效率.用4个基准函数对新算法进行试验,结果表明,新算法在稳定性和收敛性上优于基本粒子群算法.