This paper presents a study of various population partitioning techniques and their effect on the efficiency of swarm algorithms. Population partitioning techniques based on different concepts have been studied. Prominent amongst them is self-adaptive multi-population (SAMP) technique where populations are added and deleted dynamically based on their diversity. This techniques start with a single randomly initialised population, called free population. After evolution, if the distance between solutions drops below a limit, it is considered to have converged. If all existing populations have converged, a new randomly generated population is added. SAMP keeps at least one free population at all times, hence ensuring the algorithm doesn’t get trapped in local optima. Another promising population partitioning technique studied is random partitioning, where a single population is divided into many smaller sub-populations randomly. Few extensions to the studied techniques are proposed, like an adaptive hierarchical partitioning technique, seed based partitioning with fixed seeds, random partitioning with master population, SAMP with random partitioning etc. All the studied and proposed techniques are compared over a set of benchmark functions. The strongest amongst all techniques was found to be SAMPR. SAMPR is a hybrid of self-adaptive multi-population (SAMP) technique and random partitioning where after every few generations all populations are combined together and re-partitioned randomly. Efficiency of SAMPR is validated over seven well-known swarm algorithms. Extensive comparisons are conducted over multiple benchmark functions, CEC′14 function set and 800 GKLS generated functions. Results establish the efficiency of the proposed technique for improving performance of swarm algorithms.

本文介绍了各种人口分割技术及其对群体算法效率的影响。已经研究了基于不同概念的人口划分技术。其中最突出的是自适应多种群（SAMP）技术，该技术根据种群的多样性动态增加和减少种群。此技术从一个随机初始化的种群（称为自由种群）开始。演化之后，如果解之间的距离降至极限以下，则认为它已经收敛。如果所有现有种群均已收敛，则添加一个新的随机生成的种群。SAMP始终保持至少一个自由种群，因此确保算法不会陷入局部最优状态。研究的另一种有前途的人口划分技术是随机划分，将单个人口随机分为许多较小的亚群。几乎没有提出对研究技术的扩展，例如自适应分层划分技术，具有固定种子的基于种子的划分，具有主种群的随机划分，具有随机划分的SAMP等。在一组基准函数上比较了所有已研究和提出的技术。在所有技术中，最强的是SAMPR。SAMPR是自适应多种群（SAMP）技术和随机分区的混合体，其中每隔几代后，所有种群便会合并在一起并随机重新分区。SAMPR的效率已通过七个著名的群体算法进行了验证。对多个基准功能，CEC'14功能集和800 GKLS生成的功能进行了广泛的比较。