Computing all global minima of an objective function is one difficulty research area. This paper proposes a hybrid approach combined with particle swarm optimization (PSO) and biomimetic pattern recognition (BPR), while PSO is used to detect the global minima and BPR is used to remember the previously detected global minima, as well as predicting the shape of the objective function. Furthermore, repulsion technique is applied to enhance the results. Experiment results show the proposed algorithm is effective and efficient.

Through mechanism analysis of particle swarm optimization (PSO), a new modified particle swarm optimization using differential equation group--differential evolutionary particle swarm optimization (DEPSO) is introduced, and the convergence is analyzed with translation function. To enhance the diversity of swarm and improve the global convergence of PSO, PID controller is used to control dynamic evolutionary behavior of DEPSO. Simulation results proved the algorithm’s efficiency.

The paper makes an analysis on the simulated mechanisms of mind evolutionary computation (MEC) firstly and proposes an extended computation model for MEC (EMEC). EMEC manipulates the search based on the behavior space and the information space. All operations in the behavior space are processed based on groups that symbolize the solution area, while the operations in the information space are done based on the billboards that are used to record the evolutionary information. All components of EMEC are formulated in details, including the similar-taxis operation, the cooperation operation, and a simulated-annealing -based dissimilation operation (SADO). EMEC emphasizes on the share and the guide of the information in the search, and gets a performance superior to the simple MEC. The proposed EMEC was performed on some well-known benchmark problems. The experimental results show EMEC is a robust global optimization algorithm and can alleviate the premature convergence validly.

The paper develops a self-organization particle swarm optimization (SOPSO) with the aim to alleviate the premature convergence. SOPSO emphasizes the information interactions between the particle-lever and the swarm-lever, and introduce feedback to simulate the function. Through the feedback information, the particles can perceive the swarm-lever state and adopt favorable behavior model to modify their behavior, which not only can modify the exploitation and the exploration of the algorithm adaptively, but also can vary the diversity of the swarm and contribute to a global optimum output in the swarm. Relative experiments have been done; the results show SOPSO performs very well on benchmark problems, and outperforms the basic PSO in search ability.

通过分析已有的几种微粒群算法，提出了一种统一模型，并通过线性控制理论分析了其收敛性能，同时利用李亚普诺夫函数给出了模型收敛速度的上界估计。为了进一步提高算法效率，提出了两种增强全局搜索性能的参数自适应算法：单群体参数自适应微粒群算法及双群体参数自适应微粒群算法。其中单群体参数自适应微粒群算法在进化初期使用算法发散的参数设置，从而能更大程度的提高算法全局收敛能力。双群体参数自适应微粒群算法使用两个种群，一个执行全局搜索，另一个执行局部搜索，通过信息交流以提高算法性能。仿真实例证明了算法的有效性。

本文提出了一种基于模拟退火算法与微粒群算法的协同进化方法，利用了微粒群算法的易实现性、快速收敛性以及模拟退火算法的全局收敛性，通过两种算法之间的信息交流,最终得到一个高质最优解。仿真结果表明，这种协同的方法不但解的质量有很大改进，而且收敛速度也相应加快。文章还从理论上证明了该方法以概率1收敛于全局最优解。

通过分析目前Job shop问题自身的求解难点、遗传算法的特点，借鉴生物学的依据，提出了多个体交叉的遗传算法。在遗传过程中采用多个体遗传算子，充分利用个体的优良性质，并对不可行调度解根据多个体修补原则进行修正，可以保证了遗传后代的合法性和多样性，能够加快最优调度解的搜索时间。仿真结果充分证明了该算法的有效性。

在对Job shop问题进行具体描述的基础上，分析了目前利用遗传算法解决Job shop问题的各种常用的编码方法，提出了一种新的编码方法。该编码方法与主动调度的构成步骤相结合，编码中基因的排列顺序就表示各工序的优先调度顺序，由此产生可行的调度方案，基于该编码方法是以不可重复的自然数为基因进行编码，在遗传操作过程中能采用类似 TSP 的遗传算子，从而避免了非法调度解的出现。采用该编码方法在遗传算法求解 Job shop 问题的过程中，既能满足 Job shop 问题的特性，又能体现遗传算法的较强的搜索能力。仿真结果也充分证明了该算法的有效性。

在对基本PSO算法分析的基础上，提出了一种能够保证以概率1收敛于全局最优解的PSO算法—随机PSO算法（Stochastic PSO,简称SPSO），并利用 F.Solis和R.Wets 的研究结果对其全局收敛性进行了理论分析，给出了两种停止进化微粒的重新产生方法。最后以典型优化问题的实例仿真验证了SPSO算法的有效性。

微粒群算法是一种新型的进化计算方法，已在许多领域得到了广泛的应用。本文通过对基本微粒群算法的分析，发现基本微粒群算法在计算过程中由于使用了Lebesgue 侧度为0的线段进行搜索，较易得到过早收敛现象。据此，提出了一种改进的微粒群算法，该算法在运行过程中能动态调整极限位置，从而使得每个微粒的极限位置在其所经历的最好位置与整体最好位置所形成的动态圆中分布，由于在搜索空间中使用测度为正的区域对定义域空间进行搜索，能以较大概率跳出局部最优点。实例仿真结果验证了方法的正确性和有效性。