0–1 knapsack problem (KP01) is one of the classic variants of knapsack problems in which the aim is to select the items with the total profit to be in the knapsack. In contrast, the constraint of the maximum capacity of the knapsack is satisfied. KP01 has many applications in real-world problems such as resource distribution, portfolio optimization, etc. The purpose of this work is to gather the latest SA-based solvers for KP01 together and compare their performance with the state-of-the-art meta-heuristics in the literature to find the most efficient one(s). This paper not only studies the introduced and non-introduced single-solution SA-based algorithms for KP01 but also proposes a new population-based SA (PSA) for KP01 and compares it with the existing methods. Computational results show that the proposed PSA is the most efficient optimization algorithm for KP01 among all SA-based solvers. Also, PSA’s exploration and exploitation are stronger than the other SA-based algorithms since it generates several initial solutions instead of one. Moreover, it finds the neighbor solutions based on the greedy repair and improvement mechanism and uses both mutation and crossover operators to explore and exploit the solution space. Suffice to say that the next version of SA algorithms for KP01 can be enhanced by designing a population-based version of them and choosing the greedy-based approaches for the initial solution phase and local search policy.

0-1背包问题（KP01）是背包问题的经典变体之一，其目的是选择总利润在背包中的项目。相反，满足了背包最大容量的约束。KP01在现实问题中有许多应用程序，例如资源分配，资产组合优化等。此工作的目的是收集KP01的最新基于SA的求解器，并将其性能与最新的元-文献中的启发式算法以找到最有效的算法。本文不仅研究了已引入和未引入的基于单解SA的KP01算法，而且提出了一种针对KP01的新的基于种群的SA（PSA）并将其与现有方法进行比较。计算结果表明，所提出的PSA是所有基于SA的求解器中最有效的KP01优化算法。此外，PSA的探索和开发比其他基于SA的算法要强大，因为它会生成多个初始解决方案，而不是一个。此外，它基于贪婪的修复和改进机制找到邻居解决方案，并使用变异和交叉算子来探索和利用解决方案空间。可以说，可以通过设计KP01的基于种群的版本并为初始解决方案阶段和本地搜索策略选择基于贪婪的方法来增强KP01的SA算法的下一版本。PSA的探索和开发比其他基于SA的算法更强大，因为它会生成多个初始解决方案，而不是一个。此外，它基于贪婪的修复和改进机制找到邻居解决方案，并使用变异和交叉算子来探索和利用解决方案空间。可以说，可以通过设计KP01的基于种群的版本并为初始解决方案阶段和本地搜索策略选择基于贪婪的方法来增强KP01的SA算法的下一版本。PSA的探索和开发比其他基于SA的算法更强大，因为它会生成多个初始解决方案，而不是一个。此外，它基于贪婪的修复和改进机制找到邻居解决方案，并使用变异和交叉算子来探索和利用解决方案空间。可以说，可以通过设计KP01的基于种群的版本并为初始解决方案阶段和本地搜索策略选择基于贪婪的方法来增强KP01的SA算法的下一版本。