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The dismantling and protection of networks is a significant problem that has wide-ranging applications and attracts many researchers. Most current studies only focus on single-layer or one-to-one interdependent networks. However, this paper considers the more realistic case where the links between layers in interdependent networks are one-to-many, and the networks' robustness is studied accordingly. To solve the problem of dissolving interdependent networks under the premise of heterogeneous costs, we propose a cost-constrained elite quadratic evolutionary algorithm (CCEEA) based on cost constraints. Based on the network's prior information, the initial optimal feasible solutions derived from four classical algorithms are regarded as the initial elite individuals of CCEEA. The set of attack nodes is then continuously updated interactively according to a new evolutionary mechanism with flexible updates so that the combination of nodes in the final set of attack nodes can maximally facilitate the disintegration of the network. We conducted experiments on a series of representative networks and showed that on synthetic networks, the CCEEA algorithm outperforms the other four state-of-the-art attack strategies by more than 13% in terms of disintegration, which is up to 25% higher. In particular, it can be up to more than 90% higher in real networks. [ABSTRACT FROM AUTHOR]

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As one of the most popular and well-recognized clustering methods, fuzzy C-means (FCM) clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects. However, FCM algorithm is essentially a local search optimization algorithm. Therefore, sometimes, it may fail to find the global optimum. For the purpose of getting over the disadvantages of FCM algorithm, a new version of the krill herd (KH) algorithm with elitism strategy, called KHE, is proposed to solve the clustering problem. Elitism tragedy has a strong ability of preventing the krill population from degrading. In addition, the well-selected parameters are used in the KHE method instead of originating from nature. Through an array of simulation experiments, the results show that the KHE is indeed a good choice for solving general benchmark problems and fuzzy clustering analyses.

In recent years, multiobjective immune algorithms (MOIAs) have shown promising performance in solving multiobjective optimization problems (MOPs). However, basic MOIAs only use a single hypermutation operation to evolve individuals, which may induce some difficulties in tackling complicated MOPs. In this paper, we propose a novel hybrid evolutionary framework for MOIAs, in which the cloned individuals are divided into several subpopulations and then evolved using different evolutionary strategies. An example of this hybrid framework is implemented, in which simulated binary crossover and differential evolution with polynomial mutation are adopted. A fine-grained selection mechanism and a novel elitism sharing strategy are also adopted for performance enhancement. Various comparative experiments are conducted on 28 test MOPs and our empirical results validate the effectiveness and competitiveness of our proposed algorithm in solving MOPs of different types. [ABSTRACT FROM PUBLISHER]

Network disintegration or strengthening is a significant problem, which is widely used in infrastructure construction, social networks, infectious disease prevention and so on. But most studies assume that the cost of attacking anyone node is equal. In this paper, we investigate the robustness of complex networks under a more realistic assumption that costs are functions of degrees of nodes. A multi-objective, elitism-based, evolutionary algorithm (MOEEA) is proposed for the network disintegration problem with heterogeneous costs. By defining a new unit cost influence measure of the target attack node and combining with an elitism strategy, some combination nodes' information can be retained. Through an ingenious update mechanism, this information is passed on to the next generation to guide the population to move to more promising regions, which can improve the rate of convergence of the proposed algorithm. A series of experiments have been carried out on four benchmark networks and some model networks, the results show that our method performs better than five other state-of-the-art attack strategies. MOEEA can usually find min-cost network disintegration solutions. Simultaneously, through testing different cost functions, we find that the stronger the cost heterogeneity, the better performance of our algorithm. • A multi-objective disintegration algorithm considering actual cost factor is proposed. • By combining cost and node importance, a new unit cost importance measure is defined. • The combination information of nodes is added by ingenious elitism and update strategy. • Our algorithm performs better than five other state-of-the-art disintegration methods. [ABSTRACT FROM AUTHOR]

In this paper, a Modified micro Genetic Algorithm (MmGA) is proposed for undertaking Multi-objective Optimization Problems (MOPs). An NSGA-II inspired elitism strategy and a population initialization strategy are embedded into the traditional micro Genetic Algorithm (mGA) to form the proposed MmGA. The main aim of the MmGA is to improve its convergence rate towards the pareto optimal solutions. To evaluate the effectiveness of the MmGA, two experiments using the Kursawe test function in MOPs are conducted, and the results are compared with those from other approaches using a multi-objective evolutionary algorithm indicator, i.e. the Generational Distance (GD). The outcomes positively demonstrate that the MmGA is able to provide useful solutions with improved GD measures for tackling MOPs. [ABSTRACT FROM AUTHOR]

Students generally attempt to improve themselves with different manners in their spare time. Inspired by this fact, this paper presents a multi-objective elitist feedback teaching–learning-based optimization (MEFTO) algorithm for multi-objective optimization problems. To promote the exploration capacity and convergence of the basic teaching–learning-based optimization (TLBO), a feedback phase that simulates the spare-time learning phenomenon is introduced at the end of the learner phase. Students should compare their score with the average class score to select an appropriate means for further improvement. Poorly performing students can learn from the teacher directly for rapid improvement, whereas high-performing students prefer to motivate themselves for reinforcement learning. Non-dominated sorting is incorporated to permit this heuristic to solve problems with several objectives. Crowding distance calculation is adopted to maintain the diversity of the obtained solution set in a single run. Elitism strategy is also employed to provide a great improvement for the algorithm. The performance of the MEFTO algorithm is compared with seven other well-known algorithms by using a set of unconstrained benchmark test problems and three constrained engineering optimization problems. The qualitative and quantitative results indicate that the proposed algorithm can provide considerably competitive results and outperforms the other algorithms. • A novel multi-objective evolutionary algorithm (MEFTO) is developed. • The developed algorithm is evaluated on a series of benchmark functions. • The performance of the MEFTO is demonstrated using various metrics. • The developed algorithm is applied to solve three constrained engineering problem. [ABSTRACT FROM AUTHOR]

Abstract: This article presents results from an application of a genetic algorithm (GA) to the fuel reload optimization for a research reactor. In this work, we proposed an improved model of the problem and a new coding procedure for the GA to automatically search for optimal fuel loading patterns most suitable for the research reactor. The model consists of an objective function, which maximizes the effective multiplication factor and minimizes the power peaking factor, and operational and safety constraints. The new coding procedure is used to handle a constraint on the limited number of fuel shuffles in a refueling operation. The GA works with an elitist selection based on the elitism strategy and the roulette wheel spin method, a modified one-point crossover and a simple mutation. A computer program was developed in FORTRAN 90 running on a Pentium III personal computer to perform illustrative calculations for a research reactor type TRIGA MARK II. Results from illustrative calculations show that the GA can successfully search for the optimal loading patterns, which can be employed to establish a simple refueling scheme for the reactor with a limited number of fuel shuffles in a practical refueling operation. [Copyright &y& Elsevier]

The elephant herding optimization (EHO) is a recent swarm intelligence algorithm. This algorithm simulates the clan updating and separation behavior of elephants. The EHO method has been successfully deployed in various fields. However, a more reliable implementation of the standard EHO algorithm still requires improving the control and selection of the parameters, convergence speed, and efficiency of the optimal solutions. To cope with these issues, this study presents an improved EHO algorithm terms as IMEHO. The proposed IMEHO method uses a global velocity strategy and a novel learning strategy to update the velocity and position of the individuals. Furthermore, a new separation method is presented to keep the diversity of the population. An elitism strategy is also adopted to ensure that the fittest individuals are retained at the next generation. The influence of the parameters and strategies on the IMEHO algorithm is fully studied. The proposed method is tested on 30 benchmark functions from IEEE CEC 2014. The obtained results are compared with other eight metaheuristic algorithms and evaluated according to Friedman rank test. The results imply the superiority of the IMEHO algorithm to the standard EHO and other existing metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

This letter presents a pattern discovery algorithm for multi-streams mining in wireless sensor networks. The algorithm adapts operators of Genetic Algorithm with Elitism Strategy into the iteration in tracking weights. We describe the algorithm, give the explanation of correctness, and evaluate its performance using simulation. We conclude that the proposed algorithm can reduce the reconstruction error, and decrease the number of hidden variables so it is more suitable for wireless sensor networks than the existing distributed algorithms. [ABSTRACT FROM PUBLISHER]