Fast micro-differential evolution for topological active net optimization

This paper studies the optimization problem of topological active net (TAN), which is often seen in image segmentation and shape modeling. A TAN is a topological structure containing many nodes, whose positions must be optimized while a predefined topology needs to be maintained. TAN optimization is often time-consuming and even constructing a single solution is hard to do. Such a problem is usually approached by a ``best improvement local search'' (BILS) algorithm based on deterministic search (DS), which is inefficient because it spends too much efforts in nonpromising probing. In this paper, we propose the use of micro-differential evolution (DE) to replace DS in BILS for improved directional guidance. The resultant algorithm is termed deBILS. Its micro-population efficiently utilizes historical information for potentially promising search directions and hence improves efficiency in probing. Results show that deBILS can probe promising neighborhoods for each node of a TAN. Experimental tests verify that deBILS offers substantially higher search speed and solution quality not only than ordinary BILS, but also the genetic algorithm and scatter search algorithm

Hunting for Heavy Majorana Neutrinos with Lepton Number Violating Signatures at LHC

The neutrinophilic two-Higgs-doublet model ($\nu$2HDM) provides a natural way to generate tiny neutrino mass from interactions with the new doublet scalar $\Phi_\nu$ ($H^\pm,~H,~A$) and singlet neutrinos $N_R$ of TeV scale. In this paper, we perform detailed simulations for the lepton number violating (LNV) signatures at LHC arising from cascade decays of the new scalars and neutrinos with the mass order $m_{N_R}<m_{\Phi_\nu}$. Under constraints from lepton flavor violating processes and direct collider searches, their decay properties are explored and lead to three types of LNV signatures: $2\ell^\pm 4j+\cancel{E}_T$, $3\ell^\pm 4j+\cancel{E}_T$, and $3\ell^\pm\ell^\mp 4j$. We find that the same-sign trilepton signature $3\ell^\pm4j+\cancel{E}_T$ is quite unique and is the most promising discovery channel at the high-luminosity LHC. Our analysis also yields the $95\%$ C.L. exclusion limits in the plane of the $\Phi_\nu$ and $N_R$ masses at 13 (14) TeV LHC with an integrated luminosity of 100~(3000)/fb.Comment: 31 pages, 17 figures, 6 tables; v2: added a few refs and updated one ref, without other change

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