Differential evolution with an evolution path: a DEEP evolutionary algorithm

Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC'13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs

Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks

This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the NP-complete multicast routing problem (MRP). The main contribution is the extension of PSO from continuous domain to the binary or discrete domain. Firstly, a novel bi-velocity strategy is developed to represent possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP where 1 stands for a node being selected to construct the multicast tree while 0 stands for being otherwise. Secondly, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the OR-library (Operation Research Library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly as it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on GA, ACO, and PSO

4-[4-(1H-Tetra­zol-5-yl)phen­oxy]benzaldehyde

The asymmetric unit of the title compound, C14H10N4O2, contains two independent mol­ecules with similar structures. In one mol­ecule, the tetra­zole ring is oriented at dihedral angles of 17.71 (16) and 57.13 (17)°, respectively, to the central benzene ring and the terminal benzene ring; in the other mol­ecule, the corresponding dihedral angles are 16.46 (18) and 75.87 (18)°. Inter­molecular N—H⋯N hydrogen bonds and weak C—H⋯O and C—H⋯N hydrogen bonds occur in the crystal structure

4-(4-Nitro­phen­yl)morpholine

Aromatic π–π stacking inter­actions stabilize the crystal structure of the title compound, C10H12N2O3, the perpendic­ular distance between parallel planes being 3.7721 (8) Å. The morpholine ring adopts a chair comformation

Ride-hailing services can make travel easier for disadvantaged communities in low-density transit deserts

Ride-hailing services are often seen as benefiting the middle classes and other well-off Americans. But can they also serve people living in low-density areas, especially those who do not own vehicles? In new research, Shengxiao (Alex) Li, Wei Zhai, Junfeng Jiao, and Chao (Kenneth) Wang examined how ride-hailing services have reshaped transportation across neighborhoods in Austin, Texas. They find that while ride-hailing services mainly serve those who live downtown, where transit services are available, they also have the potential to help people living in low-income and low-density neighborhoods, and those without vehicles

1-[4-(2-Chloro­eth­oxy)-2-hy­droxy­phen­yl]ethanone

In the title compound, C10H11ClO3, obtained by the reaction of 2,4-dihy­droxy­acetophenone, potassium carbonate and 1-bromo-2-chloro­ethane, an intra­molecular O—H⋯O hydrogen bond occurs

Annealing Effect on Photovoltaic Performance of CdSe Quantum-Dots-Sensitized TiO2 Nanorod Solar Cells

Large area rutile TiO₂ nanorod arrays were grown on F:SnO₂ (FTO) conductive glass using a hydrothermal method at low temperature. CdSe quantum dots (QDs) were deposited onto single-crystalline TiO₂ nanorod arrays by a chemical bath deposition (CBD) method to make a photoelectrode. The solar cell was assembled using a CdSe-TiO₂ nanostructure as the photoanode and polysulfide solution as the electrolyte. The annealing effect on optical and photovoltaic properties of CdSe quantum-dotssensitized TiO₂ nanorod solar cells was studied systematically. A significant change of the morphology and a regular red shift of band gap of CdSe nanoparticles were observed after annealing treatment. At the same time, an improved photovoltaic performance was obtained for quantum-dots-sensitized solar cell using the annealed CdSe-TiO₂ nanostructure electrode. The power conversion efficiency improved from 0.59% to 1.45% as a consequence of the annealing effect. This improvement can be explained by considering the changes in the morphology, the crystalline quality, and the optical properties caused by annealing treatment

An evolutionary algorithm with double-level archives for multiobjective optimization

Existing multiobjective evolutionary algorithms (MOEAs) tackle a multiobjective problem either as a whole or as several decomposed single-objective sub-problems. Though the problem decomposition approach generally converges faster through optimizing all the sub-problems simultaneously, there are two issues not fully addressed, i.e., distribution of solutions often depends on a priori problem decomposition, and the lack of population diversity among sub-problems. In this paper, a MOEA with double-level archives is developed. The algorithm takes advantages of both the multiobjective-problemlevel and the sub-problem-level approaches by introducing two types of archives, i.e., the global archive and the sub-archive. In each generation, self-reproduction with the global archive and cross-reproduction between the global archive and sub-archives both breed new individuals. The global archive and sub-archives communicate through cross-reproduction, and are updated using the reproduced individuals. Such a framework thus retains fast convergence, and at the same time handles solution distribution along Pareto front (PF) with scalability. To test the performance of the proposed algorithm, experiments are conducted on both the widely used benchmarks and a set of truly disconnected problems. The results verify that, compared with state-of-the-art MOEAs, the proposed algorithm offers competitive advantages in distance to the PF, solution coverage, and search speed

Many-qubit network employing cavity QED in a decoherence-free subspace

We propose a many-qubit network with cavity QED by encoding qubits in decoherence-free subspace, based on which we can implement many-logic-qubit conditional gates by means of cavity assisted interaction with single-photon pulses. Our scheme could not only resist collective dephasing errors, but also much reduce the implementational steps compared to conventional methods doing the same job, and we can also complete communications between two arbitrary nodes. We show the details by implementing a three-logic-qubit Toffoli gate, and explore the experimental feasibility and challenge based on currently achievable cavity QED technologies.Comment: 5 pages, 3 figures. to be published in Phys. Rev.