Battery-free Wireless Sensor Networks (BF-WSNs) have emerged as an essential part of Internet of Things (IoT) systems. Although two-dimensional (2D) BF-WSNs have been researched, three-dimensional (3D) ones are more indicative of real-world applications. Achieving$k$-coverage in this scenario is a greater challenge and has yet to be investigated. This paper presents an optimization problem in heterogeneous 3D BF-WSNs to maximize$k$-coverage quality while considering the recharging and sampling rates. We prove the problem is NP-Hard and decouple it into two subproblems. The first optimizes$k$-coverage quality for each time slot without energy constraints. The second ensures that nodes scheduled for operation in each time slot can be immediately replenished with sufficient energy. We prove the near-optimal and optimal solutions to the two subproblems composes the near-optimal solution to the original problem. Following the development of Distributed Iterative Grouping (DIG) algorithm and Adaptive Sampling (AS) method to address two subproblems each, we propose a sleep scheduling algorithm to integrate them and solve the original problem. Simulation results verify the effectiveness and efficiency of the proposed algorithm.

In order to solve the problem of subset community in some community detection algorithms which is based on label propagation, this paper proposes a method that merges the core network in advance, and constructs a scholars recommendation model for academic social network. The model processes the problem of subset community through the GraphChi framework in parallel and recommends the scholar users within the community according to the relevant recommendation rules to achieve the scholars' recommendation service. Experimental results show that the proposed model effectively reduces the running time of community detection algorithm and achieves good recommendation results.

Virtual network embedding (VNE), which is a significant problem in cloud computing, has gained much attention among many researchers recently. Due to the NP-hardness of VNE, the existing solvers are either inefficient or inaccurate. This paper develops a novel algorithm based on the ant colony system (ACS). To solve the VNE problem, the algorithm structure concentrates on the link mapping from virtual network to substrate network. Particularly, for a specific virtual network request, we first sort the embedding sequence of virtual nodes according to their link resources. Then, ACS is used to embed the virtual nodes onto substrate nodes according the sorted sequence, while the virtual links are mapped via a shortest path strategy for the embedded nodes. For the first time, we propose a link resource heuristic information and incorporate it into the search process of ACS. The link resource heuristic information has two significant effects, one is to make virtual nodes tend to be embedded on the substrate nodes that cost less bandwidth, and the other is to confirm the connectivity of the substrate nodes that embed the virtual nodes. The proposed algorithm improves the optimization performance of VNE when compared with a few existing algorithms, while it substantially reduces the cost of time.

Differential evolution (DE) is a simple and efficient evolutionary algorithm for global optimization. In distributed differential evolution (DDE), the population is divided into several sub-populations and each sub-population evolves independently for enhancing population diversity as well as algorithmic performance. Sub-populations in DDE share their elite individuals with neighborhood through a predefined migration topology. However, the construction of traditional migration topologies does not consider the position information of sub-populations in the search space. The position information is helpful in controlling the degree of diversity between the sub-populations and their migrated individuals. A proper degree of diversity could promote the balance between exploration and exploitation for DDE algorithms. To achieve this target, a dynamic space-driven migration topology is proposed in this paper. The proposed topology is constructed and updated according to the distances between sub-populations. Based on this proposed topology, some sub-populations receive diverse individuals from neighborhood far away while others communicate with neighborhood nearby. Numerical experiments have been performed on 13 diverse test functions. Results verify the advantage of DDE with the proposed migration topology compared to those with classic topologies.

Cognitive diagnostic models (CDMs) are a new class of test models developed for educational assessment. They have gained growing attention in recent years for their distinctive ability to provide detailed feedback about examinees' ability. Automatic test assembly (ATA), as in other test models, has been one of the most critical issues in the development and applications of CDMs. However, developing ATA methods for CDMs is especially challenging because no close-form expressions can measure the quality of a test form based on the items used. Although some heuristic methods have been proposed for building a single test form of CDMs, few ATA methods can construct uniform test forms of CDMs, in which each test form contains a different set of items but meets equivalent demand of test quality. In order to fill the gap, this paper proposes a novel genetic algorithm (GA) for constructing uniform test forms of CDMs. The effectiveness and efficiency of the proposed method is validated on a synthetic item pool under different conditions.

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for "learning." This leads to a generalized "learning PSO" paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO.

Cloud computing offers unprecedented capacity to execute large-scale workflows in the “era of big data”. In 2014, a cost-minimization and deadline-constrained workflow scheduling (CMDCWS) model is firstly proposed by Rodriguez and Buyya, which is applicable for the business need of cloud computing that a workflow task should be finished by minimizing the execute cost within a deadline constraint. As scheduling cloud computing resources for workflow is an NP-hard problem, Rodriguez and Buyya proposed to use particle swarm optimization (PSO) to solve the CMDCWS problem. In traditional PSO for CMDCWS, each dimension in the particle position stands for each task and the value of the corresponding dimension stands for the index of the cloud resource that executes this task. However, this may have drawback because the value of each dimension does not relate to the resource characteristic but is only a meaningless index number. Therefore the learning behaviors among the particles do not make sense because learning from index number may not lead to better position. In this paper, we present a resource renumber strategy to encode the particle position and design a renumber PSO (RNPSO) for CMDCWS. In RNPSO, all the resources are re-ordered and re-numbered according to their computational ability, i.e., the cost per unit time. By this, the values of particle position can make sense and the positions difference between the well-performed and poorly-performed particles can guide poorly-performed particle to promising region. We conduct experiments on test cases with small, middle, and large scales to compare the performance of PSO and RNPSO. The results show that the resource renumber strategy is promising for enhancing the PSO performance.

This paper proposes to solve the dynamic optimization problem (DOP) by using an adaptive particle swarm optimization (APSO) algorithm with an variable relocation strategy (VRS). The VRS based APSO algorithm (APSO/VRS) has the following two advantages when solving DOP. Firstly, by using the APSO optimizing framework, the algorithm benefits from the fast optimization speed due to the adaptive parameter control. More importantly, the adaptive parameter and operator in APSO make the algorithm fast respond to the environment changes of DOP. Secondly, VRS was reported in the literature to help dynamic evolutionary algorithm (DEA) to relocate the individual position in promising region when environment changes. Therefore, the modified VRS used in APSO can collect historical information in the stability stage and use such information to guide the particle variable relocation in the change stage. We evaluated both APSO and APSO/VRS on several dynamic benchmark problems and compared with two state-of-the-art DEAs and DEA that also used the VRS. The results show that both APSO and APSO/VRS can obtain very competitive results on these problems, and APSO/VRS outperforms others on most of the test cases.

Evolution mechanisms of different biological and social systems have inspired a variety of evolutionary computation (EC) algorithms. However, most existing EC algorithms simulate the evolution procedure at the individual-level. This paper proposes a new EC mechanism inspired by the evolution procedure at the tribe-level, namely tribal ecosystem inspired algorithm (TEA). In TEA, the basic evolution unit is not an individual that represents a solution point, but a tribe that covers a subarea in the search space. More specifically, a tribe represents the solution set locating in a particular subarea with a coding structure composed of three elements: tribal chief, attribute diversity, and advancing history. The tribal chief represents the locally best-so-far solution, the attribute diversity measures the range of the subarea, and the advancing history records the local search experience. This way, the new evolution unit provides extra knowledge about neighborhood profiles and search history. Using this knowledge, TEA introduces four evolution operators, reforms, self-advance, synergistic combination, and augmentation, to simulate the evolution mechanisms in a tribal ecosystem, which evolves the tribes from potentially promising subareas to the global optimum. The proposed TEA is validated on benchmark functions. Comparisons with three representative EC algorithms confirm its promising performance.

Differential evolution (DE) has been demonstrated to be one of the most promising evolutionary algorithms (EAs) for global numerical optimization. DE mainly differs from other EAs in that it employs difference of the parameter vectors in mutation operator to search the objective function landscape. Therefore, the performance of a DE algorithm largely depends on the design of its mutation strategy. In this paper, we propose a new kind of DE mutation strategies whose greediness degree can be adaptively adjusted. The proposed mutation strategies utilize the information of top t solutions in the current population. Such a greedy strategy is beneficial to fast convergence performance. In order to adapt the degree of greediness to fit for different optimization scenarios, the parameter t is adjusted in each generation of the algorithm by an adaptive control scheme. This way, the convergence performance and the robustness of the algorithm can be enhanced at the same time. To evaluate the effectiveness of the proposed adaptive greedy mutation strategies, the approach is applied to original DE algorithms, as well as DE algorithms with parameter adaptation. Experimental results indicate that the proposed adaptive greedy mutation strategies yield significant performance improvement for most of cases studied.

This paper proposes a path planner for autonomous underwater vehicles (AUVs) in 3-D underwater space. We simulate an underwater space with rugged seabed and suspending obstacles, which is close to real world. In the proposed representation scheme, the problem space is decomposed into parallel subspaces and each subspace is described by a grid method. The paths of AUVs are simplified as a set of successive points in the problem space. By jointing these waypoints, the entire path of the AUV is obtained. A cost function with penalty method takes into account the length, energy consumption, safety and curvature constraints of AUVs. It is applied to evaluate the quality of paths. Differential evolution (DE) algorithm is used as a black-box optimization tool to provide optimal solutions for the path planning. In addition, we adaptively adjust the parameters of DE according to population distribution and the blockage of parallel subspaces so as to improve its performance. Experiments are conducted on 6 different scenarios. The results validate that the proposed algorithm is effective for improving solution quality and avoiding premature convergence.

Traditional multiobjective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multiobjective optimization problems (MOPs). However, this consideration may cause difficulty to assign fitness to individuals because different objectives often conflict with each other. In order to avoid this difficulty, this paper proposes a novel coevolutionary technique named multiple populations for multiple objectives (MPMO) when developing MOEAs. The novelty of MPMO is that it provides a simple and straightforward way to solve MOPs by letting each population correspond with only one objective. This way, the fitness assignment problem can be addressed because the individuals' fitness in each population can be assigned by the corresponding objective. MPMO is a general technique that each population can use existing optimization algorithms. In this paper, particle swarm optimization (PSO) is adopted for each population, and coevolutionary multiswarm PSO (CMPSO) is developed based on the MPMO technique. Furthermore, CMPSO is novel and effective by using an external shared archive for different populations to exchange search information and by using two novel designs to enhance the performance. One design is to modify the velocity update equation to use the search information found by different populations to approximate the whole Pareto front (PF) fast. The other design is to use an elitist learning strategy for the archive update to bring in diversity to avoid local PFs. CMPSO is comprehensively tested on different sets of benchmark problems with different characteristics and is compared with some state-of-the-art algorithms. The results show that CMPSO has superior performance in solving these different sets of MOPs.

In most of the applications of wireless sensor networks(WSN), covering the area of interest and delivering the sensed information to the sink are two fundamental functions. Extensive research associated with these two issues, such as energy efficient coverage and delay-constraint routing, can be found in the literature. However, few works combine these two issues together. Considering the fact that wireless sensors can take the responsibility of both sensing and routing, it is expected that a solution jointly considering these two issues will provide more benefit. In this paper, we consider the problem: how to find a routing path in a WSN with the maximum sensing coverage provided by the nodes on the path subject to the delay constraint. We first proved that this problem is NP-hard and then proposed a distributed algorithm based on Monte-Carlo integration method and label setting(LS) algorithm. Analysis and simulation results show that under the same time delay constraint, the proposed algorithm can find a routing path with significantly larger sensing coverage (more than $87\%$ in our simulation) than that was obtained considering only hop constraint.