Purpose

The purpose of this paper is to modify the crow search algorithm (CSA) to enhance both exploration and exploitation capability by including two novel approaches. The positions of the crows are updated in two approaches based on awareness probability (AP). With AP, the position of a crow is updated by considering its velocity, calculated in a similar fashion to particle swarm optimization (PSO) to enhance the exploiting capability. Without AP, the crows are subdivided into groups by considering their weights, and the crows are updated by conceding leaders of the groups distributed over the search space to enhance the exploring capability. The performance of the proposed PSO-based group-oriented CSA (PGCSA) is realized by exploring the solution of benchmark equations. Further, the proposed PGCSA algorithm is validated over recently published algorithms by solving engineering problems.

Design/methodology/approach

In this paper, two novel approaches are implemented in two phases of CSA (with and without AP), which have been entitled the PGCSA algorithm to solve engineering benchmark problems.

Findings

The proposed algorithm is applied with two types of problems such as eight benchmark equations without constraint and six engineering problems.

Originality/value

The PGCSA algorithm is proposed with superior competence to solve engineering problems. The proposed algorithm is substantiated hypothetically by using a paired t-test.

中文翻译：

---

基于PSO的面向群体的乌鸦搜索算法（PGCSA）

目的

本文的目的是通过包括两种新颖的方法来修改乌鸦搜索算法（CSA），以增强勘探和开发能力。基于意识概率（AP），可通过两种方法更新乌鸦的位置。使用AP，可以通过考虑乌鸦的速度来更新乌鸦的位置，乌鸦的速度以与粒子群优化（PSO）类似的方式进行计算，以增强利用能力。如果没有AP，则通过考虑乌鸦的权重将其分为几类，并通过让分布在搜索空间中的各小组的负责人让乌鸦更新，以增强探索能力。通过探索基准方程的解决方案，可以实现所提出的基于PSO的基于组的CSA（PGCSA）的性能。进一步，

设计/方法/方法

本文在CSA的两个阶段（带有和不带有AP）中实现了两种新颖的方法，它们被称为PGCSA算法来解决工程基准问题。

发现

该算法适用于两种类型的问题，例如八个无约束的基准方程和六个工程问题。

创意/价值

提出了具有卓越能力的PGCSA算法来解决工程问题。假设的算法通过使用配对t检验得到了证实。