Yiqiao Cai

Differential evolution with hybrid linkage crossover

In the field of evolutionary algorithms (EAs), differential evolution (DE) has been the subject of much attention due to its strong global optimization capability and simple implementation. However, in most DE algorithms, crossover operator often ignores the consideration of interactions between pairs of variables. That is, DE is linkage-blind, and the problem-specific linkages are not utilized effectively to guide the search process. Furthermore, linkage learning techniques have been verified to play an important role in EA optimization. Therefore, to alleviate the drawback of linkage-blind in DE and enhance its performance, a novel linkage utilization technique, called hybrid linkage crossover (HLX), is proposed in this study. HLX utilizes the perturbation-based method to automatically extract the linkage information of a specific problem and then uses the linkage information to guide the crossover process. By incorporating HLX into DE, the resulting algorithm, named HLXDE, is presented. In order to evaluate the effectiveness of HLXDE, HLX is incorporated into six original DE algorithms, as well as several advanced DE variants. Experimental results demonstrate the high performance of HLX for the DE algorithms studied.

Adaptive direction information in differential evolution for numerical optimization

Differential evolution (DE) is a powerful evolutionary algorithm (EA) for numerical optimization. It has been successfully used in various scientific and engineering fields. In most of the DE algorithms, the neighborhood and direction information are not fully and simultaneously exploited to guide the search. Most recently, to make full use of these information, a DE framework with neighborhood and direction information (NDi-DE) was proposed. It was experimentally demonstrated that NDi-DE was effective for most of the DE algorithms. However, the performance of NDi-DE heavily depends on the selection of direction information. To alleviate this drawback and improve the performance of NDi-DE, the adaptive operator selection (AOS) mechanism is introduced into NDi-DE to adaptively select the direction information for the specific DE mutation strategy. Therefore, a new DE framework, adaptive direction information based NDi-DE (aNDi-DE), is proposed in this study. With AOS, the good balance between exploration and exploitation of aNDi-DE can be dynamically achieved. In order to evaluate the effectiveness of aNDi-DE, the proposed framework is applied to the original DE algorithms, as well as several advanced DE variants. Experimental results show that aNDi-DE is able to adaptively select the most suitable type of direction information for the specific DE mutation strategy during the evolutionary process. The efficiency and robustness of aNDi-DE are also confirmed by comparing with NDi-DE.

Following Targets for Mobile Tracking in Wireless Sensor Networks

Traditional tracking solutions in wireless sensor networks based on fixed sensors have several critical problems. First, due to the mobility of targets, a lot of sensors have to keep being active to track targets in all potential directions, which causes excessive energy consumption. Second, when there are holes in the deployment area, targets may fail to be detected when moving into holes. Third, when targets stay at certain positions for a long time, sensors surrounding them have to suffer heavier work pressure than do others, which leads to a bottleneck for the entire network. To solve these problems, a few mobile sensors are introduced to follow targets directly for tracking because the energy capacity of mobile sensors is less constrained and they can detect targets closely with high tracking quality. Based on a realistic detection model, a solution of scheduling mobile sensors and fixed sensors for target tracking is proposed. Moreover, the movement path of mobile sensors has a provable performance bound compared to the optimal solution. Results of extensive simulations show that mobile sensors can improve tracking quality even if holes exist in the area and can reduce energy consumption of sensors effectively.

Multiobjective evolutionary algorithm for frequency assignment problem in satellite communications

Satellite communications technology leads to an important improvement in our life and world. The frequency assignment problem (FAP) is a fundamental problem in satellite communication system for providing high-quality transmissions. The whole goal of the FAP in satellite communication system is to minimize co-channel interference between two satellite systems by rearranging frequency assignment. Recently, many metaheuristics, including neural networks and evolutionary algorithms, are proposed for this NP-complete problem. All such algorithms formulate the FAP as a single-objective problem, although it obviously has two objectives and thus essentially is a multiobjective optimization problem. This study explicitly formulates FAP as a multiobjective optimization problem and presents a multiobjective evolutionary algorithm based on decomposition (MOEA/D) with a problem-specific subproblem-dependent heuristic assignment (SHA), called MOEA/D-SHA, for the multiobjective FAP. Simulation results show that the MOEA/D-SHA outperforms significantly general-purpose MOEA/D, and an off-the-shelf multiobjective algorithm, i.e., NSGA-II. The advantages of the MOEA/D-SHA over the state-of-the-art single-objective approaches are also shown.

Continuous tracking for mobile targets with mobility nodes in WSNs

Tracking mobile targets is one of the most important applications in wireless sensor networks (WSNs). Traditional tracking solutions are based on fixed sensor nodes and have two critical problems. First, in WSNs, the energy constraint is a main concern, but due to the mobility of targets, lots of sensor nodes in WSNs have to switch between active and sleep states frequently, which causes excessive energy consumption. Second, when there are holes in the deployment area, targets may fail to be detected while moving in the holes. To solve these problems, this paper exploits a few of mobile sensor nodes to continuously track mobile targets because the energy capacity of mobile nodes is less constrained. Based on a realistic detection model, a solution for scheduling mobile nodes to cooperate with ordinary fixed nodes is proposed. When targets move, mobile nodes move along with them for tracking. The results of extensive simulations show that mobile nodes help to track the target when holes appears in the coverage area and extend the effective monitoring time. Moreover, the proposed solution can effectively reduce the energy consumption of sensor nodes and prolong the lifetime of the networks.

Trajectory Privacy Preservation Based on a Fog Structure for Cloud Location Services

The development of mobile cloud computing technology has made location-based service (LBS) increasingly more popular. Given the continuous requests to cloud LBS servers, the amounts of location and trajectory information collected by LBS servers are continuously increasing. Privacy awareness for LBS has been extensively studied in recent years. Among the privacy concerns about LBS, trajectory privacy preservation is particularly important. Based on privacy preservation models, previous work have mainly focused on peer-to-peer and centralized architectures. However, the burden on users is heavy in peer-to-peer architectures, because user devices need to communicate with LBS servers directly. In centralized architectures, a trusted third party (TTP) is introduced, and acts as a bridge between users and the LBS server. Anonymity technologies, such as k-anonymity, mix-zone, and dummy technologies, are usually implemented by the TTP to ensure safety. There are certain drawbacks in TTP architectures: Users have no physical control of the TTP. Moreover, the TTP is more attractive to adversaries, because substantially more sensitive information is stored by the TTP. To solve the above-mentioned problems, in this paper, we propose a fog structure to store partial important data with the dummy anonymity technology to ensure physical control, which can be considered as absolutely trust. Compared with cloud computing, fog computing is a promising technique that extends the cloud computing to the edge of a network. Moreover, fog computing provides local computation and storage abilities, wide geo-distribution, and support for mobility. Therefore, mobile users’ partial important information can be stored on a fog server to ensure better management. We take the principles of similarity, intersection, practicability, and correlation into consideration and design a dummy rotation algorithm with several properties. The effectiveness of the proposed method is validated through extensive simulations, which show that the proposed method can provide enhanced privacy preservation.

A novel improved particle swarm optimization algorithm based on individual difference evolution

Abstract As a well-known stochastic optimization algorithm, the particle swarm optimization (PSO) algorithm has attracted the attention of many researchers all over the world, which has resulted in many variants of the basic algorithm, in addition to a vast number of parameter selection/control strategies. However, most of these algorithms evolve their population using a single fixed pattern, thereby reducing the intelligence of the entire swarm. Some PSO-variants adopt a multimode evolutionary strategy, but lack dynamic adaptability. Furthermore, competition among particles is ignored, with no consideration of individual thinking or decision-making ability. This paper introduces an evolution mechanism based on individual difference, and proposes a novel improved PSO algorithm based on individual difference evolution (IDE-PSO). This algorithm allocates a competition coefficient called the emotional status to each particle for quantifying individual differences, separates the entire swarm into three subgroups, and selects the specific evolutionary method for each particle according to its emotional status and current fitness. The value of the coefficient is adjusted dynamically according to the evolutionary performance of each particle. A modified restarting strategy is employed to regenerate corresponding particles and enhance the diversity of the population. For a series of benchmark functions, simulation results show the effectiveness of the proposed IDE-PSO, which outperforms many state-of-the-art evolutionary algorithms in terms of convergence, robustness, and scalability.

Cellular direction information based differential evolution for numerical optimization: an empirical study

Differential evolution (DE) is a well-known evolutionary algorithm which has been successfully applied in many scientific and engineering fields. In most DE algorithms, the base and difference vectors for mutation are randomly selected from the current population. That is, the useful population information cannot be fully exploited to guide the search of DE through mutation. Furthermore, the selection of parents in mutation has been verified to be critical for the DE performance. Therefore, to alleviate this drawback and improve the performance of DE, a novel DE algorithm with a directional mutation based on cellular topology is proposed in this study. This proposed algorithm is named as Cellular Direction Information based DE (DE-CDI). In DE-CDI, the cellular topology is employed first to define a neighborhood for each individual of population and then the direction information based on the neighborhood is incorporated into the mutation operator. In this way, DE-CDI not only utilizes the neighborhood information to exploit the regions of better individuals and accelerate convergence, but also introduces the direction information to guide the search to the promising area. To evaluate the performance of the proposed method, DE-CDI is applied to the original DE algorithms, as well as several advanced DE variants. Experimental results demonstrate the high performance of DE-CDI for most DE algorithms studied.

Differential evolution with guiding archive for global numerical optimization

Graphical abstractDisplay Omitted HighlightsThis study presents a DE framework with guiding archive to help DE escape from the situation of stagnation.The proposed framework is general and can be applied to most DEs.The proposed framework is applied to six original DE algorithms, as well as two advanced DE variants.The proposed framework is evaluated experimentally on 28 benchmark functions and 8 real-world application problems. Differential evolution (DE) is a simple, yet efficient, population-based global evolutionary algorithm. DE may suffer from stagnation. This study presents a DE framework with guiding archive (GAR-DE) to help DE escape from the situation of stagnation. The proposed framework constructs a guiding archive and executes stagnation detection at each iteration. Guiding archive is composed of a certain number of relatively high-quality solutions. These solutions are collected in terms of fitness as well as diversity. If a stagnated individual is detected, the proposed framework selects a solution from guiding archive to replace the base vector in mutation operator. In this way, more promising solutions are provided to guide the evolution and effectively help DE escape from the situation of stagnation. The proposed framework is applied to six original DE algorithms, as well as two advanced DE variants. Experimental results on 28 benchmark functions and 8 real-world application problems show that the proposed framework can enhance the performance of most DE algorithms studied.

Enabling public auditability for operation behaviors in cloud storage

In this paper, we focus on auditing for users’ operation behaviors, which is significant for the avoidance of potential crimes in the cloud and equitable accountability determination in the forensic. We first present a public model for operation behaviors in cloud storage, in which a trusted third party is introduced to verify the integrity of operation behavior logs to enhance the credibility of forensic results as well as alleviate the burden of the forensic investigator. Further, we design a block-based logging approach to support selective verification and a hash-chain-based structure for each log block to ensure the forward security and append-only properties for log entries. Moreover, to achieve the tamper resistance of log blocks and non-repudiation of auditing proofs, we employ Merkle hash tree (MHT) to record the hash values of the aggregation authentication block tags sequentially and publish the root of MHT to the public once a block has been appended. Meanwhile, using the authentication property of MHT, our scheme can provide log-less verification with privacy preservation. We formally prove the security of the proposed scheme and evaluate its performance on entry appending and verification by concrete experiments and comparisons with the state-of-the-art schemes. The results demonstrate that the proposed scheme can effectively achieve secure auditing for log files of operation behaviors in cloud storage and outperforms the previous ones in computation complexity and communication overhead.