用粒子群在周期搜索空间中飞行处理数值优化问题的边界约束

用自适应松弛规则处理群算法中的等式约束

Handling equality constraints by adaptive relaxing rule for swarm algorithms

针对群算法处理等式约束问题，提出了自适应约束松弛规则。这类问题的可行空间可能类似于岭函数类，这给群算法的应用带来了困难。为了更容易进入解空间，引入了松弛拟可行空间，并自适应收缩。在基准函数上的实验结果与其他算法的性能进行了比较，证明了该算法的有效性。

Constraint-Handling Techniques for Particle Swarm Optimization Algorithms

Population-based methods can cope with a variety of different problems, including problems of remarkably higher complexity than those traditional methods can handle. The main procedure consists of successively updating a population of candidate solutions, performing a parallel exploration instead of traditional sequential exploration. While the origins of the PSO method are linked to bird flock simulations, it is a stochastic optimization method in the sense that it relies on random coefficients to introduce creativity, and a bottom-up artificial intelligence-based approach in the sense that i…

Particle Swarms Reformulated towards a Unified and Flexible Framework

The Particle Swarm Optimisation (PSO) algorithm has undergone countless modifications and adaptations since its original formulation in 1995. Some of these have become mainstream whereas many others have not been adopted and faded away. Thus, a myriad of alternative formulations have been proposed to the extent that the question arises as to what the basic features of an algorithm must be to belong in the PSO family. The aim of this paper is to establish what defines a PSO algorithm and to attempt to formulate it in such a way that it encompasses many existing variants. Therefore, different ve…

Refined Particle Swarm Intelligence Method for Abrupt Motion Tracking

Conventional tracking solutions are not feasible in handling abrupt motion as they are based on smooth motion assumption or an accurate motion model. Abrupt motion is not subject to motion continuity and smoothness. To assuage this, we deem tracking as an optimisation problem and propose a novel abrupt motion tracker that based on swarm intelligence - the SwaTrack. Unlike existing swarm-based filtering methods, we first of all introduce an optimised swarm-based sampling strategy to tradeoff between the exploration and exploitation of the search space in search for the optimal proposal distribu…

A Study of the Fundamental Parameters of Particle Swarm Optimizers

The range of applications of traditional optimization methods are limited by the features of the object variables, and of both the objective and the constraint functions. In contrast, population-based algorithms whose optimization capabilities are emergent properties, such as evolutionary algorithms and particle swarm optimization, present almost no restriction on those features and can handle different optimization problems with few or no adaptations. Their main drawbacks consist of their comparatively higher computational cost and difficulty in handling equality constraints. The particle swa…

追求卓越：SHOA——一种面向多模态问题的具有竞争力的伯劳优化算法

In Search of Excellence: SHOA as a Competitive Shrike Optimization Algorithm for Multimodal Problems

针对现有群智能算法在多模态困难问题中难以找到最优解的问题，受伯劳鸟集群生存繁育行为启发提出SHOA算法，经41个测试函数和4个工程问题验证，其性能优于多数同类自然启发式算法。