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Bi-objective constraint-handling technique may be one of the most promising constraint techniques for constrained optimization problems. It regards the constraints as an extra objective and using Pareto ranking as selection operator. These algorithms achieve a good convergence by utilizing potential infeasible individuals, but not be good at maintaining the diversity of the population. It is significant to balance the diversity of the population and the convergence of the algorithm. This paper proposes a novel constraint-handling technique based on biased dynamic weights for constrained evolutionary algorithm. The biased weights are used to select different individuals with low objective values and low degree of constraint violations. Furthermore, along with the evolution, more emphasis is placed on the individuals with lower objective values and lower degree of constraint violations by adjusting the biased weights dynamically, which forces the search to a promising feasible region. Thus, the proposed algorithm can keep a good balance between the convergence and the diversity of the population. Moreover, we compared the proposed algorithm with other state-of-the-art algorithms on 42 benchmark problems. The experimental results showed the reliability and stabilization of the proposed algorithm.

The wideband code division multiple access (WCDMA) network planning problem requires to determine the location and the configuration parameters of the base stations (BSs) so as to maximize the capacity and minimize the installation cost. This problem can be formulated as a complex set covering problem. Compared to the classical set covering problems, the coverage area of each BS is unknown in advance. This makes that the selection of each BS location and configuration parameters is determined by the location and configuration parameters of the neighbor BSs. Accordingly, we will conduct a competition and cooperation model based on the re-covered area of the BSs to measure the relationship of the BSs. Then, an efficient genetic operation based on this model is proposed to generate new-quality solutions. Further, four BS configuration parameters, i.e., the antenna height, antenna tilt, sector orientation and pilot signal power, are taken into account as well. Since there are too many combination levels of the configuration parameters, an encoding method based on orthogonal design is presented to reduce the search space. Subsequently, we merge the proposed encoding method and genetic operation into the multiobjective evolutionary algorithm-based decomposition (MOEA/D-M2M) to solve the WCDMA network planning problem. Simulation results show the efficacy of the proposed encoding and genetic operation in comparison with the existing counterpart.

There are several studies on hybrid multi-operator recombination methods, while few works have been proposed in the area of combining different fitness assignment in a framework. On the other hand, it is known that fitness assignment has a marked impact on the performance of evolutionary multiobjective optimization algorithm (EMOA). In this paper, a hybrid EMOA is proposed, which divides the population into several smaller subpopulations according to their distribution in the objective space. Each subpopulation is evolved by an individual EMOA, and a hybrid performance measure estimates the performance of these EMOAs. We focus on the fitness assignment and assume that all EMOAs used in the subpopulations adopt the same recombination operator. To evaluate performance of the proposed algorithm, we compare it with MOEA/D-M2M, MOE-A/D, SMS-EMOA and NSGA-II on 16 test instances. Experimental results show that the proposed algorithm performs better than or similar to those compared EMOAs.

This paper presents a new multi-objective artificial bee colony algorithm called dMOABC by dividing the whole searching space S into two independent parts S 1 and S 2. In this algorithm, two "basic" colonies are assigned to search potential solutions in regions S 1 and S 2, while the so-called "synthetic" colony explores in S. This multi-colony model could enable the good diversity of the population, and three colonies share information in a special way. A fixed-size external archive is used to store the non-dominated solutions found so far. The diversity over the archived solutions is controlled by utilizing a self-adaptive grid. For basic colonies, neighbor information is used to generate new food sources. For the synthetic colony, besides neighbor information, the global best food source gbest selected from the archive, is also adopted to guide the flying trajectory of both employed and onlooker bees. The scout bees are used to get rid of food sources with poor qualities. The proposed algorithm is evaluated on a set of unconstrained multi-objective test problems taken from CEC09, and is compared with 11 other state-of-the-art multi-objective algorithms by applying Friedman test in terms of four indicators: HV, SPREAD, EPSILON and IGD. It is shown by the test results that our algorithm significantly surpasses its competitors.

This paper studies the problem of underdetermined blind source separation with the nonstrictly sparse condition. Different from current approaches in literature, we propose a new and more effective algorithm to estimate the mixing matrices resulted from noise output data sets. After we introduce a clustering prototype of orthogonal complement space and give an extension of the normal vector clustering prototype, a new method combing the fuzzy clustering and eigenvalue decomposition technique to estimate the mixing matrix is presented in order to deal with the nonstrictly sparse situation. A convergent algorithm for estimating the mixing matrices is established, and numerical simulations are given to demonstrate the effectiveness of the proposed approach.

Li Dong Wang and colleagues report a genome wide association study for esophageal squamous cell carcinoma in the Chinese population. They identify two risk loci at PLCE1 and C20orf54.

In vitro shoot proliferation from stem disc of Allium chinense, a vegetatively propagated plant, was investigated in this experiment. In the present study, shoots were formed directly on stem discs on a medium containing 1 mg/l N6-benzyladenine (BA) and 0.5 mg/lα-naphthaleneacetic acid (NAA). These shoots were further cultured on MS media supplemented with various levels of BA in combination with NAA, and new shoot clusters developed easily from the explants cultured despite considerable differences in the induction of shoot clusters with different levels of BA and NAA. The most productive combination of growth regulators proved to be 1.0 mg/l BA and 1.0 mg/l NAA, in which about 17 shoots were produced per cluster in 8 weeks culture. Most of the formed shoots were rooted 15 days after being cultured on MS media supplemented with 0.1–1.0 mg/l NAA. The survival rate of the plantlets under ex vitro conditions was 95% in pots filled with a peat: sand (2:1 v/v) mixture after two weeks. In vitro bulblet formation were strongly promoted by the high temperature of 30°C compared to that at 25, 20 and 15°C, and 12% (w/v) sucrose appeared to be optimal for bulblet development. Results from this study demonstrated that A. chinense could be in vitro propagated by using stem discs and in vitro bulblet formation could be achieved.

Nat. Genet. 42, 759–763 (2010); published online 22 August 2010; corrected after print 27 August 2014 In contrast to the version of this article initially published, the authors now find no evidence to support association with esophageal squamous cell carcinoma susceptibility for rs13042395[T] at 20p13 in their original data, in two independent sets of cases and controls collected in other Chinese populations or in the joint analysis of these three studies.