A survey on the Metaheuristics for Cryptanalysis of Substitution and Transposition Ciphers

This paper presents state-of-art cryptanalysis studies on attacks of the substitution and transposition ciphers using various metaheuristic algorithms. Traditional cryptanalysis methods employ an exhaustive search, which is computationally expensive. Therefore, metaheuristics have attracted the interest of researchers in the cryptanalysis field. Metaheuristic algorithms are known for improving the search for the optimum solution and include Genetic Algorithm, Simulated Annealing, Tabu Search, Particle Swarm Optimization, Differential Evolution, Ant Colony, the Artificial Bee Colony, Cuckoo Search, and Firefly algorithms. The most important part of these various applications is deciding the fitness function to guide the search. This review presents how these algorithms have been implemented for cryptanalysis purposes. The paper highlights the results and findings of the studies and determines the gaps in the literature.     

基于DE的ε-SVRM参数优化研究

参数优化是ε-支持向量回归机研究领域的重要问题,其本质是一个优化搜索的过程。基于差异演化算法在求解优化问题上的有效性,提出了以差异演化算法寻优技巧的ε 支持向量回归机参数优化方法。将该算法应用于受噪声影响的标准函数,与采用遗传算法、蚁群算法、粒子群算法对支持向量机进行优化的仿真实验结果对比表明由DE算法所确定的ε 支持向量回归机具有较好的预测性能。     

Binary BAT algorithm and RBFN based hybrid credit scoring model

Credit scoring is a process of calculating the risk associated with an applicant on the basis of applicant’s credentials such as social status, financial status, etc. and it plays a vital role to improve cash flow for financial industry. However, the credit scoring dataset may have a large number of irrelevant or redundant features which leads to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with huge number of features. This work emphasized on the role of feature selection and proposed a hybrid model by combining feature selection by utilizing Binary BAT optimization technique with a novel fitness function and aggregated with for Radial Basis Function Neural Network (RBFN) for credit score classification. Further, proposed feature selection approach is aggregated with Support Vector Machine (SVM) & Random Forest (RF), and other optimization approaches namely: Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), Hybrid Particle Swarm Optimization and Genetic Algorithm (PSOGA), Improved Krill Herd (IKH), Improved Cuckoo Search (ICS), Firefly Algorithm (FF) and Differential Evolution (DE) are also applied for comparative analysis.

     

新型群智能优化算法综述

传统群智能算法在解决复杂实际多目标优化问题中存在不足,近年来学者提出诸多新型群智能算法,适用性强,在求解复杂实际问题中取得了较好的实验效果。以算法提出时间为主线,对新型群智能算法中细菌觅食优化算法、混合蛙跳算法、人工蜂群算法、萤火虫算法、布谷鸟搜索、果蝇优化算法和头脑风暴优化算法的改进及应用进行分析和综述,并对群智能算法未来的研究发展方向进行了探讨。     

A comparative review on mobile robot path planning: Classical or meta-heuristic methods?

The involvement of Meta-heuristic algorithms in robot motion planning has attracted the attention of researchers in the robotics community due to the simplicity of the approaches and their effectiveness in the coordination of the agents. This study explores the implementation of many meta-heuristic algorithms, e.g. Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) in multiple motion planning scenarios. The study provides comparison between multiple meta-heuristic approaches against a set of well-known conventional motion planning and navigation techniques such as Dijkstra’s Algorithm (DA), Probabilistic Road Map (PRM), Rapidly Random Tree (RRT) and Potential Field (PF). Two experimental environments with difficult to manipulate layouts are used to examine the feasibility of the methods listed. several performance measures such as total travel time, number of collisions, travel distances, energy consumption and displacement errors are considered for assessing feasibility of the motion planning algorithms considered in the study. The results show the competitiveness of meta-heuristic approaches against conventional methods. Dijkstra ’s Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.     

Kapur’s entropy based optimal multilevel image segmentation using Crow Search Algorithm

Image segmentation is an essential part of image analysis, which has a direct impact on the quality of image analysis results. Thresholding is one of the simplest and widely used methods for image segmentation. Thresholding can be either bi-level, which involves partitioning of an image into two segments, or multilevel, which partitions an image into multiple segments using multiple thresholds values. This paper focuses on multilevel thresholding. A good segmentation scheme through multilevel thresholding identifies suitable threshold values to optimize between-class variance or entropy criterion. For such optimizations, nature inspired metaheuristic algorithms are commonly used. This paper presents a Kapur’s entropy based Crow Search Algorithm (CSA) to estimate optimal values of multilevel thresholds. Crow Search Algorithm is based on the intelligent behavior of crow flock. Crow Search Algorithm have shown better results because of less number of parameters, no premature convergence, and better exploration–exploitation balance in the search strategy. Kapur’s entropy is used as an objective function during the optimization process. The experiments have been performed on benchmarked images for different threshold values (i.e. 2, 4, 8, 16, 32 thresholds). The proposed method has been assessed and performance is compared with well-known metaheuristic optimization methods like Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO) and Cuckoo Search (CS). Experimental results have been evaluated qualitatively and quantitatively by using well-performed evaluation methods namely PSNR, SSIM, and FSIM. Computational time and Wilcoxon p-type value also compared. Experimental results show that proposed algorithm performed better than PSO, DE, GWO, MFO and CS in terms of quality and consistency.     

Roosters Algorithm: A Novel Nature-Inspired Optimization Algorithm

Some species of females, e.g., chicken, bird, fish etc., might mate with more than one males. In the mating of these polygamous creatures, there is competition between males as well as among their offspring. Thus, male reproductive success depends on both male competition and sperm rivalry. Inspired by this type of sexual life of roosters with chickens, a novel nature-inspired optimization algorithm called Roosters Algorithm (RA) is proposed. The algorithm was modelled and implemented based on the sexual behavior of roosters. 13 well-known benchmark optimization functions and 10 IEEE CEC 2018 test functions are utilized to compare the performance of RA with the performance of well-known algorithms; Standard Genetic Algorithm (SGA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Grey Wolf Optimizer (GWO). Also, non-parametric statistical tests, Friedman and Wilcoxon Signed Rank Tests, were performed to demonstrate the significance of the results. In 20 of the 23 functions that were tested, RA either offered the best results or offered similar results to other compared algorithms. Thus, in this paper, we not only present a novel nature-inspired algorithm, but also offer an alternative method to the well-known algorithms commonly used in the literature, at least as effective as them.     

Multi-Cohort Intelligence algorithm: an intra- and inter-group learning behaviour based socio-inspired optimisation methodology

ABSTRACT

A Multi-Cohort Intelligence (Multi-CI) metaheuristic algorithm in emerging socio-inspired optimisation domain is proposed. The algorithm implements intra-group and inter-group learning mechanisms. It focusses on the interaction amongst different cohorts. The performance of the algorithm is validated by solving 75 unconstrained test problems with dimensions up to 30. The solutions were comparing with several recent algorithms such as Particle Swarm Optimisation (PSO), Covariance Matrix Adaptation Evolution Strategy, Artificial Bee Colony, Self-Adaptive Differential Evolution Algorithm, Comprehensive Learning Particle Swarm Optimisation, Backtracking Search Optimisation Algorithm, and Ideology Algorithm. The Wilcoxon signed-rank test was carried out for the statistical analysis and verification of the performance. The proposed Multi-CI outperformed these algorithms in terms of the solution quality including objective function value and computational cost, i.e. computational time and functional evaluations. The prominent feature of the Multi-CI algorithm along with the limitations is discussed as well. In addition, an illustrative example is also solved and every detail is provided.     

Cryptanalysis of Vigenere cipher using Cuckoo Search

Evolutionary techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS) are promising nature-inspired meta-heuristic optimization algorithms. Cuckoo Search combined with Lévy flights behavior and Markov chain random walk can search global optimal solution very quickly. The aim of this paper is to investigate the applicability of Cuckoo Search algorithm in cryptanalysis of Vigenere cipher. It is shown that optimal solutions obtained by CS are better than the best solutions obtained by GA or PSO for the analysis of the Vigenere cipher. The results show that a Cuckoo Search based attack is very effective on the Vigenere cryptosystem.     

Hybridization of Chaos and Flower Pollination Algorithm over K-Means for data clustering

Classical clustering algorithms like K-means often converge to local optima and have slow convergence rates for larger datasets. To overcome such situations in clustering, swarm based algorithms have been proposed. Swarm based approaches attempt to achieve the optimal solution for such problems in reasonable time. Many swarm based algorithms such as Flower Pollination Algorithm (FPA), Cuckoo Search Algorithm (CSA), Black Hole Algorithm (BHA), Bat Algorithm (BA) Particle Swarm Optimization (PSO), Firefly Algorithm (FFA), Artificial Bee Colony (ABC) etc have been successfully applied to many non-linear optimization problems. In this paper, an algorithm is proposed which hybridizes Chaos Optimization and Flower Pollination over K-means to improve the efficiency of minimizing the cluster integrity. The proposed algorithm referred as Chaotic FPA (CFPA) is compared with FPA, CSA, BHA, BA, FFA, and PSO over K-Means for data clustering problem. Experiments are conducted on sixteen benchmark datasets. Algorithms are compared on four different performance parameters — cluster integrity, execution time, number of iterations to converge (NIC) and stability. Results obtained are analyzed statistically using Non-parametric Friedman test. If Friedman test rejects the Null hypothesis then pair wise comparison is done using Nemenyi test. Experimental Result demonstrates the following: (a) CFPA and BHA have better performance on the basis of cluster integrity as compared to other algorithms; (b) Prove the superiority of CFPA and CSA over others on the basis of execution time; (c) CFPA and FPA converges earlier than other algorithms to evaluate optimal cluster integrity; (d) CFPA and BHA produce more stable results than other algorithms.     

针对混合变量优化问题的协同进化蚁群优化算法

针对由连续变量和分类变量构成的混合变量优化问题（MVOP）,采用协同进化策略来对混合变量决策空间进行搜索,提出了一种协同进化蚁群优化算法（CACOA_MV）。CACOA_MV分别采用连续和离散蚁群优化（ACO）策略生成连续和分类变量子种群,通过合作者来对连续和分类变量子向量进行评价,分别对连续和分类变量子种群进行更新来实现对混合变量决策空间的高效协同搜索。进一步地,利用信息素平滑机制增强对分类变量解空间的全局探索能力,并设计了一种面向协同进化框架的“最佳+随机合作者”的重启策略来提高协同搜索效率。与混合变量的蚁群（ACO_MV）算法和种群规模线性变小的差分进化-蚁群混合变量优化算法（L-SHADE_ACO）的比较表明,CACOA_MV能够进行更有效的局部开发,从而提高最终结果在目标空间中的近似精度;与基于集合的混合变量差分进化算法（DE_MV）相比较,CACOA_MV能够在决策空间中更好地逼近全局最优解,具有更好的全局探索能力。综上,采用协同进化机制的CACOA_MV能有效保持全局探索和局部开发的平衡,从而具有更好的寻优性能。     

基于自适应布谷鸟搜索算法的K-means聚类算法及其应用

针对原始K-means聚类算法受初始聚类中心影响过大以及容易陷入局部最优的不足,提出一种基于改进布谷鸟搜索（CS）的K-means聚类算法（ACS-K-means）。其中,自适应CS（ACS）算法在标准CS算法的基础上引入步长自适应调整,以提高搜索精度和收敛速度。在UCI标准数据集上,ACS-K-means算法可得到比K-means、基于遗传算法的K-means（GA-K-means）、基于布谷鸟搜索的K-means（CS-K-means）和基于粒子群优化的K-means（PSO-K-means）算法更优的聚类质量和更高的收敛速度。将ACS-K-means聚类算法应用到南宁市青秀区“城管通”系统的城管案件热图的开发中,在地图上对案件地理坐标进行聚类并显示,应用结果表明,聚类效果良好,算法收敛速度快。     

群体智能算法在图像分割中的应用综述

图像分割的通用方法一直是图像处理领域中的热点和难点。随着人工智能的兴起和发展,群体智能算法成为当下热点研究的方向,将图像分割技术结合群体智能算法成为一种新型有效的改进方法。群智能算法通过模拟自然界的事物或生物的行动规律,将传统的人工智能和群体生物结合,在解空间中搜索最优解,为解决复杂问题提供了新的解决思路。阐述群体智能算法的研究现状和发展过程,将早期的蚁群算法（Ant Colony Optimization,ACO）、经典的粒子群算法（Particle Swarm Optimization Algorithm,PSO）以及较新的麻雀搜索算法（Sparrow Search Algorithm,SSA）为例详细介绍其算法原理方法,并简要表述蝙蝠算法（Bat Algorithm,BA）、鲸鱼优化算法（Whale Optimization Algorithm,WOA）、人工蜂群算法（Artificial Bee Colony Algorithm,ABC）、萤火虫算法（Firefly Algorithm,FA）、布谷鸟搜索法（Cuckoo Search,CS）、细菌觅食算法（Bacterial Foraging Optimization,BFO）和最新的蜉蝣算法（Mayfly Algorithm,MA）的原理,在此基础上,结合国内外文献对上述算法的改进方法和结合图像分割技术的综合改进及应用进行分析总结。将群体智能算法结合图像分割技术的代表性算法提取出来进行列表分析总结,随后概述总结群体智能算法的统一框架、共同特性、不同的差异并提出存在的问题,最后对未来趋势做出展望。     

Energy Efficient Cluster Head Selection for Internet of Things

Recently, Internet of Things (IoT) devices are highly utilized in diverse fields such as environmental monitoring, industries, and smart home, among others. Under such instances, a cluster head is selected among the diverse IoT devices of wireless sensor network (WSN) based IoT network to maintain a reliable network with efficient data transmission. This article proposed a novel method with the combination of Gravitational Search Algorithm (GSA) and Artificial Bee Colony (ABC) algorithm to accomplish the efficient cluster head selection. This method considers the distance, energy, delay, load, and temperature of the IoT devices during the operation of the cluster head selection process. Furthermore, the performance of the proposed method is analyzed by comparing with conventional methods such as Artificial Bee Colony (ABC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and GSO algorithms. The analysis related to the existence of the number of alive nodes, convergence estimation, and performance in terms of normalized energy, load, and temperature of the IoT devices are determined. Thus the analysis of our implementation reveals the superior performance of the proposed method.     

A new hybrid differential evolution algorithm with self-adaptation for function optimization

In this study, a new hybrid algorithm, hDEBSA, is proposed with the aid of two evolutionary algorithms, Differential Evolution (DE) and Backtracking Search Optimization Algorithm (BSA). The control parameters of both algorithms are simultaneously considered as a self-adaptation basis such that the values of the parameters update automatically during the optimization process to improve performance and convergence speed. To validate the proposed algorithm, twenty-eight CEC2013 test functions are considered. The performance results of hDEBSA are validated by comparing them with several state-of-the-art algorithms that are available in literature. Finally, hDEBSA is applied to solve four real-world optimization problems, and the results are compared with the other algorithms, where it was found that the hDEBSA performance is better than that of the other algorithms.     

Multilevel thresholding based on Chaotic Darwinian Particle Swarm Optimization for segmentation of satellite images

This paper proposes an improved variant of Darwinian Particle Swarm Optimization algorithm based on chaotic functions. Most of the evolutionary algorithms faces the problem of getting trapped in local optima in its search for global optimum solutions. This is highly influenced by the use of random sequences by different operators in these algorithms along their run. The proposed algorithm replaces random sequences by chaotic sequences mitigating the problem of premature convergence. Experiments were conducted to investigate the efficiency of 10 defined chaotic maps and the best one was chosen. Performance of the proposed Chaotic Darwinian Particle Swarm Optimization (CDPSO) algorithm is compared with chaotic variants of optimization algorithms like Cuckoo Search, Harmony Search, Differential Evolution and Particle Swarm Optimization exploiting the chosen optimal chaotic map. Various histogram thresholding measures like minimum cross entropy and Tsallis entropy were used as objective functions and implemented for satellite image segmentation scenario. The experimental results are validated qualitatively and quantitatively by evaluating the mean, standard deviation of the fitness values, PSNR, MSE, SSIM and the total time required for the execution of each optimization algorithm.     

Efficient protocol for data clustering by fuzzy Cuckoo Optimization Algorithm

Data clustering is a technique for grouping similar and dissimilar data. Many clustering algorithms fail when dealing with multi-dimensional data. This paper introduces efficient methods for data clustering by Cuckoo Optimization Algorithm; called COAC and Fuzzy Cuckoo Optimization Algorithm, called FCOAC. The COA by inspire of cuckoo bird nature life tries to solve continuous problems. This algorithm clusters a large dataset to prior determined clusters numbers by this meta-heuristic algorithm and optimal the results by fuzzy logic. Firstly, the algorithm generates a random solutions equal to cuckoo population and with length dataset objects and with a cost function calculates the cost of each solution. Finally, fuzzy logic tries for the optimal solution. The performance of our algorithm is evaluated and compared with COAC, Black hole, CS, K-mean, PSO and GSA. The results show that our algorithm has better performance in comparison with them.     

Hybrid chaos optimization and affine scaling search algorithm for solving linear programming problems

In this study, we addressed Single Objective Linear Programming (SOLP). This article proposed a new combination of Chaos Optimization Algorithm (COA) with Affine Scaling Search (AFS) to be used as a Hybrid COA and AFS algorithm (Chaos AFS) for solving SOLP. The potential of COA as an emerging optimization algorithm to improve efficiency and effectiveness of AFS is investigated. Chaos AFS method is so-called numerical search algorithm that searches through the domain of decision variables of SOLP to obtain final feasible solution. An initial solution point, obtained from COA, will be used as starting solution point in AFS algorithm to improve the performance of AFS algorithm. The result shows that Hybrid COA and AFS for solving SOLP problems significantly improves the results of objective value compared to pure AFS and reduces the number of iteration steps compared to simplex and pure AFS.     

Culture-based Artificial Bee Colony with heritage mechanism for optimization of Wireless Sensors Network

In this paper, a hybridization model based on culture algorithm and Artificial Bee Colony is proposed. The objective of the hybrid model is mainly to get benefit of the previous knowledge gained by predecessor foragers which help bees searching for food sources in potential positions. The proposed CB-ABC focuses on the kind of information in the belief space that should be stored to reduce promising solutions’ area. Moreover, CB-ABC divides the population into two groups of individuals, one group updates by the heritage of best previous solutions and the other group updates by self-adaptive information. The performance of the new algorithm has been validated on a variety of numerical testbench functions (ranging from CEC 2005 and CEC 2017) and compared to standard ABC and other variants named ABCM, HPA and CABCA as well.The proposed algorithm proves its success as well when applied on Wireless Sensor Network (WSN) to locate them accurately. To validate the performance of CB-ABC it is compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo Search (CS), Firefly Algorithm (FA) and Parallel Firefly Algorithm (PFA). CB-ABC shows the least average error value compared to the standard ABC as well as the other algorithms previously mentioned in the comparison. Moreover, CB-ABC succeeds in reducing the number of iterations as well as function evaluations to 17% of those of the standard ABC to 20% of those obtained by ABCM during solving WSN localization problem. CB-ABC’s parametric study (heritage size and offspring ratio) has been carried out as well.     

基于差分演化策略的混沌乌鸦算法求解折扣{0-1}背包问题

针对确定性算法难于求解的各项的重量系数和价值系数在大范围内取值的折扣{0-1}背包问题（D{0-1}KP）,提出了基于差分演化策略的混沌乌鸦算法（DECCSA）。首先,采用混沌映射生成初始乌鸦种群;然后,采用混合编码方式和贪心修复与优化策略（GROS）解决了D{0-1}KP的编码问题;最后,引入差分演化策略提高算法的收敛速度。对4类大规模D{0-1}KP实例的计算结果表明：DECCSA比遗传算法、细菌觅食算法和变异蝙蝠算法求得的最好值和平均值更优,能得到最优解或更好的近似解,非常适于求解D{0-1}KP。