Based on the algorithm structure, each metaheuristic algorithm may have its pros and cons, which may result in high performance in some problems and low functionality in some others. The idea is to hybridize two or more algorithms to cover each other’s weaknesses. In this study, particle swarm optimization (PSO), simulated annealing (SA) and differential evolution (DE) are combined to develop a more powerful search algorithm. First, the temperature concept of SA is applied to balance the exploration/exploitation capability of the hybridized algorithm. Then, the DE’s mutation operator is used to improve the exploration capability of the algorithm to escape the local minimums. Next, DE’s mutation operator has been modified so that past experiences can be used for smarter mutations. Finally, the PSO particles’ tendency to their local optimums or the global optimum, which balances the algorithm’s random and greedy search, is affected by the temperature. The temperature influences the algorithm’s behavior so that the random search is more significant at the beginning, and the greedy search becomes more important as the temperature is reduced. The results are compared with the basic PSO, SA, DE, cuckoo search (CS), and hybridized CS-PSO algorithm on 20 benchmark problems. The comparison reveals that, in most cases, the new algorithm outperforms others.

基于算法结构，每个元启发式算法可能都有其优缺点，这可能导致某些问题中的性能较高，而另一些方面的功能较低。想法是将两种或多种算法混合使用，以弥补彼此的弱点。在这项研究中，粒子群优化（PSO），模拟退火（SA）和差分进化（DE）相结合，以开发出更强大的搜索算法。首先，应用温度概念SA来平衡混合算法的探索/开发能力。然后，使用DE的变异算子来提高算法的探索能力，以逃避局部最小值。接下来，对DE的变异算子进行了修改，以便可以将过去的经验用于更聪明的变异。最后，PSO粒子趋于其局部最优或全局最优的趋势（平衡算法的随机搜索和贪婪搜索）受温度影响。温度会影响算法的行为，因此，随机搜索在开始时会更加重要，而温度降低时，贪婪搜索将变得更加重要。将结果与20个基准问题上的基本PSO，SA，DE，布谷鸟搜索（CS）和混合CS-PSO算法进行了比较。比较表明，在大多数情况下，新算法的性能优于其他算法。随着温度降低，贪婪搜索变得更加重要。将结果与20个基准问题上的基本PSO，SA，DE，布谷鸟搜索（CS）和混合CS-PSO算法进行了比较。比较表明，在大多数情况下，新算法的性能要优于其他算法。随着温度降低，贪婪搜索变得更加重要。将结果与20个基准问题上的基本PSO，SA，DE，布谷鸟搜索（CS）和混合CS-PSO算法进行了比较。比较表明，在大多数情况下，新算法的性能优于其他算法。