Novel multi-objective optimization algorithm

Many multi-objective evolutionary algorithms （MOEAs） can converge to the Pareto optimal front and work well on two or three objectives, but they deteriorate when faced with manyobjective problems. Indicator-based MOEAs, which adopt various indicators to evaluate the fitness values （instead of the Paretodominance relation to select candidate solutions）, have been regarded as promising schemes that yield more satisfactory results than well-known algorithms, such as non-dominated sort- ing genetic algorithm （NSGA-II） and strength Pareto evolutionary algorithm （SPEA2）. However, they can suffer from having a slow convergence speed. This paper proposes a new indicatorbased multi-objective optimization algorithm, namely, the multi- objective shuffled frog leaping algorithm based on the ε indicator （ε-MOSFLA）. This algorithm adopts a memetic meta-heuristic, namely, the SFLA, which is characterized by the powerful capability of global search and quick convergence as an evolutionary strategy and a simple and effective E-indicator as a fitness assignment scheme to conduct the search procedure. Experimental results, in comparison with other representative indicator-based MOEAs and traditional Pareto-based MOEAs on several standard test problems with up to 50 objectives, show that ε-MOSFLA is the best algorithm for solving many-objective optimization problems in terms of the solution quality as well as the speed of convergence.     

混合蛙跳算法在云计算资源调度的策略改进

混合蛙跳算法已在云计算资源调度有所运用。针对青蛙种群初始化随机性大、局部搜索盲目、容易陷入局部最优的问题,提出了一种混合蛙跳算法在云计算资源调度的改进策略。该改进策略首先运用SY-MM算法和随机生成方式结合的方法对种群进行初始化,生成适应度较好且保持多样性的青蛙种群;然后对传统蛙跳算法局部搜索中步长公式进行改进,使得能够自适应的去更新步长,进而提升局部搜索能力。通过实验证明改进算法对于云计算中资源调度的时间和负载平衡方面有良好的优化性能。     

混合蛙跳优化决策面的LSB±k隐写算法

针对现有智能优化改进隐写不能对高维特征同时进行优化的问题,提出了一种混合蛙跳优化决策面的改进LSB±k隐写算法(记为SFLA-LSB±k).不同于其他优化改进隐写中尽可能减少图像载密前后某种特征变化的策略,在SFLA-LSB±k中,通过优化载密图像的特征变化,使载密图像特征变化方向随机化,导致分类器无法训练出一个能对载体与载密图像进行分类的决策面,从而达到抵抗分析的目的.实验结果表明,与标准的LSB±k隐写和相关PSO优化改进LSB±k隐写相比,SFLA-LSB±k有效提高了LSB±k的安全性,特别是当k取1时,该算法针对78维特征隐写分析的AUC值可下降到0.5637.     

非劣解变异蛙跳算法

针对基本蛙跳算法搜索速度和精度不高的缺点,将变异的思路融入基本蛙跳算法,提出了一种非劣解变异蛙跳算法.算法充分利用蛙群的群体信息,对青蛙子族群中的若干非劣解结合自身信息和群体信息进行变异,避免了算法陷入局部最优,并大幅度提高了算法的搜索速度.实验表明,改进后的算法收敛速度以及收敛精度方面都比基本蛙跳算法有了很大程度的提高,同时,该算法与相关文献中的算法进行比较发现,其性能有较大的提高.