Conventional optimization methods are not efficient enough to solve many of the naturally complicated optimization problems. Thus, inspired by nature, metaheuristic algorithms can be utilized as a new kind of problem solvers in solution to these types of optimization problems. In this paper, an optimization algorithm is proposed which is capable of finding the expected quality of different locations and also tuning its exploration-exploitation dilemma to the location of an individual. A novel particle swarm optimization algorithm is presented which implements the conditioning learning behavior so that the particles are led to perform a natural conditioning behavior on an unconditioned motive. In the problem space, particles are classified into several categories so that if a particle lies within a low diversity category, it would have a tendency to move towards its best personal experience. But, if the particle’s category is with high diversity, it would have the tendency to move towards the global optimum of that category. The idea of the birds’ sensitivity to its flying space is also utilized to increase the particles’ speed in undesired spaces in order to leave those spaces as soon as possible. However, in desirable spaces, the particles’ velocity is reduced to provide a situation in which the particles have more time to explore their environment. In the proposed algorithm, the birds’ instinctive behavior is implemented to construct an initial population randomly or chaotically. Experiments provided to compare the proposed algorithm with the state-of-the-art methods show that our optimization algorithm is one of the most efficient and appropriate ones to solve the static optimization problems.

中文翻译：

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无约束优化的自适应粒子群算法

传统的优化方法效率不足以解决许多自然复杂的优化问题。因此，受自然界的启发，元启发式算法可用作解决这类优化问题的新型问题求解器。在本文中，提出了一种优化算法，该算法能够找到不同位置的预期质量，并将其勘探开发难题调整为个人的位置。提出了一种新颖的粒子群优化算法，该算法实现了条件学习行为，从而导致了粒子对非条件动机进行自然的条件调节。在问题空间中，粒子被分为几类，因此，如果粒子位于低多样性类别中，它会倾向于朝着最好的个人体验发展。但是，如果粒子的类别具有较高的多样性，则它将倾向于朝该类别的全局最优方向发展。鸟类对飞行空间的敏感性的想法也被用来提高不希望空间中粒子的速度，以便尽快离开这些空间。但是，在理想的空间中，粒子的速度会降低，从而导致粒子有更多时间探索其环境的情况。在提出的算法中，实现了鸟类的本能行为以随机或混沌地构造初始种群。