The main feature of large-scale multi-objective optimization problems (LSMOP) is to optimize multiple conflicting objectives while considering thousands of decision variables at the same time. An efficient LSMOP algorithm should have the ability to e
scape the local optimal solution from the huge search space and find the global optimal. Most of the current researches focus on how to deal with decision variables. However, due to the large number of decision variables, it is easy to lead to high computational cost. Maintaining the diversity of the population is one of the effective ways to improve search efficiency. In this paper, we propose a probabilistic prediction model based on trend prediction model and generating-filtering strategy, called LT-PPM, to tackle the LSMOP. The proposed method enhances the diversity of the population through importance sampling. At the same time, due to the adoption of an individual-based evolution mechanism, the computational cost of the proposed method is independent of the number of decision variables, thus avoiding the problem of exponential growth of the search space. We compared the proposed algorithm with several state-of-the-art algorithms for different benchmark functions. The experimental results and complexity analysis have demonstrated that the proposed algorithm has significant improvement in terms of its performance and computational efficiency in large-scale multi-objective optimization.
Collision phenomena are ubiquitous and of importance in determining the microscopic structures and intermolecular interactions of atoms and molecules. The existing approaches are mostly based on atomic or molecular scatterings, which are hindered by
the inconvenience of using ultra-high vacuum and low temperature systems. Here we demonstrate a new spin-noise spectroscopic approach by measuring optical polarization rotation noise of the probe light, which operates with simple apparatus and ambient conditions. Our approach features tens of gigahertz bandwidth and one part-per-million resolution, outperforming existing spin-noise techniques. Enabled by the new technique, we observe the collision-induced spin noise of alkali atoms, and precisely determine key collision parameters, such as collision diameter, well depth, and dominant interaction type. Our work provides a new tool to study a broad range of collision phenomena under ambient conditions.
Interacting quantum systems are attracting increasing interest for developing precise metrology. In particular, the realisation that quantum-correlated states and the dynamics of interacting systems can lead to entirely new and unexpected phenomena h
ave initiated an intense research effort to explore interaction-based metrology both theoretically and experimentally. However, the current framework of interaction-based metrology mainly focuses on single-parameter estimations, a demonstration of multiparameter metrology using interactions as a resource was heretofore lacking. Here we demonstrate an interaction-based multiparameter metrology with strongly interacting nuclear spins. We show that the interacting spins become intrinsically sensitive to all components of a multidimensional field when their interactions are significantly larger than their Larmor frequencies. Using liquid-state molecules containing strongly interacting nuclear spins, we demonstrate the proof-of-principle estimation of all three components of an unknown magnetic field and inertial rotation. In contrast to existing approaches, the present interaction-based multiparameter sensing does not require external reference fields and opens a path to develop an entirely new class of multiparameter quantum sensors.
Development of new techniques to search for particles beyond the standard model is crucial for understanding the ultraviolet completion of particle physics. Several hypothetical particles are predicted to mediate exotic spin-dependent interactions be
tween particles of the standard model that may be accessible to laboratory experiments. However, laboratory searches are mostly conducted for static spin-dependent interactions, with only a few experiments so far addressing spin- and velocity-dependent interactions. Here, we demonstrate a search for exotic spin- and velocity-dependent interactions with a spin-based amplifier. Our technique makes use of hyperpolarized nuclear spins as a pre-amplifier to enhance the effect of pseudo-magnetic field produced by exotic interactions by an amplification factor of > 100. Using such a spin-based amplifier, we establish constraints on the spin- and velocity-dependent interactions between polarized and unpolarized nucleons in the force range of 0.03-100 m. Our limits represent at least two orders of magnitude improvement compared to previous experiments. The established technique can be further extended to investigate other exotic spin-dependent interactions.
Real-world multiobjective optimization problems usually involve conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto optimal front (POF) when the environment changes. In recent years, ev
olutionary algorithms based on prediction models have been considered promising. However, most existing approaches only make predictions based on the linear correlation between a finite number of optimal solutions in two or three previous environments. These incomplete information extraction strategies may lead to low prediction accuracy in some instances. In this paper, a novel prediction algorithm based on incremental support vector machine (ISVM) is proposed, called ISVM-DMOEA. We treat the solving of dynamic multiobjective optimization problems (DMOPs) as an online learning process, using the continuously obtained optimal solution to update an incremental support vector machine without discarding the solution information at earlier time. ISVM is then used to filter random solutions and generate an initial population for the next moment. To overcome the obstacle of insufficient training samples, a synthetic minority oversampling strategy is implemented before the training of ISVM. The advantage of this approach is that the nonlinear correlation between solutions can be explored online by ISVM, and the information contained in all historical optimal solutions can be exploited to a greater extent. The experimental results and comparison with chosen state-of-the-art algorithms demonstrate that the proposed algorithm can effectively tackle dynamic multiobjective optimization problems.
The critical quantum metrology, which exploits the quantum phase transition for high precision measurement, has gained increasing attention recently. The critical quantum metrology with the continuous quantum phase transition, however, is experimenta
lly very challenging since the continuous quantum phase transition only exists at the thermal dynamical limit. Here, we propose an adiabatic scheme on a perturbed Ising spin model with the first order quantum phase transition. By employing the Landau-Zener anticrossing, we can not only encode the unknown parameter in the ground state but also tune the energy gap to control the evolution time of the adiabatic passage. We experimentally implement the adiabatic scheme on the nuclear magnetic resonance and show that the achieved precision attains the Heisenberg scaling. The advantages of the scheme-easy implementation, robust against the decay, tunable energy gap-are critical for practical applications of quantum metrology.
Superradiant phase transition (SPT) in thermal equilibrium, as a fundamental concept bridging the statistical physics and electrodynamics, can offer the key resources for quantum information science. Notwithstanding its fundamental and practical sign
ificances, equilibrium SPT has never been observed in experiments since the first proposal in the 1970s. Furthermore, the existence of equilibrium SPT in the cavity quantum electrodynamics (QED) systems is still subject of ongoing debates, due to the no-go theorem induced by the so-called A2 term. Based on the platform of nuclear magnetic resonance (NMR), here we experimentally demonstrate the occurrence of equilibrium SPT beyond no-go theorem by introducing the antisqueezing effect. The mechanism relies on the antisqueezing that recovers the singularity of the ground state via exponentially enhancing the zero point fluctuation (ZPF) of system. The strong entanglement and squeezed Schrodinger cat states of spins are achieved experimentally in the superradiant phase, which may play an important role in fundamental tests of quantum theory, implementing quantum metrology and high-efficient quantum information processing. Our experiment also shows the antisqueezing-enhanced signal-to-noise rate (SNR) of NMR spectrum, providing a general method for implementing high-precision NMR experiments.
We investigate the potential stochastic gravitational waves from first-order electroweak phase transitions in a model with pseudo-Nambu-Goldstone dark matter and two Higgs doublets. The dark matter candidate can naturally evade direct detection bound
s, and can achieve the observed relic abundance via the thermal mechanism. Three scalar fields in the model obtain vacuum expectation values, related to phase transitions at the early Universe. We search for the parameter points that can cause first-order phase transitions, taking into account the existed experimental constraints. The resulting gravitational wave spectra are further evaluated. Some parameter points are found to induce strong gravitational wave signals, which have the opportunity to be detected in future space-based interferometer experiments LISA, Taiji, and TianQin.
Ultralight axion-like particles (ALPs) are well-motivated dark matter candidates introduced by theories beyond the standard model. However, the constraints on the existence of ALPs through existing laboratory experiments are hindered by their current
sensitivities, which are usually weaker than astrophysical limits. Here, we demonstrate a new quantum sensor to search for ALPs in the mass range that spans about two decades from 8.3 feV to 744 feV. Our sensor makes use of hyperpolarized long-lived nuclear spins as a pre-amplifier that effectively enhances coherently oscillating axion-like dark-matter field by a factor of >100. Using spin-based amplifiers, we achieve an ultrahigh magnetic sensitivity of 18 fT/Hz$^{1/2}$, which is significantly better than state-of-the-art nuclear-spin magnetometers. Our experiment constrains the parameter space describing the coupling of ALPs to nucleons over our mass range, at 67.5 feV reaching $2.9times 10^{-9}~textrm{GeV}^{-1}$ ($95%$ confidence level), improving over previous laboratory limits by at least five orders of magnitude. Our measurements also constrain the ALP-nucleon quadratic interaction and dark photon-nucleon interaction with new limits beyond the astrophysical ones
Robot gait optimization is the task of generating an optimal control trajectory under various internal and external constraints. Given the high dimensions of control space, this problem is particularly challenging for multi-legged robots walking in c
omplex and unknown environments. Existing literatures often regard the gait generation as an optimization problem and solve the gait optimization from scratch for robots walking in a specific environment. However, such approaches do not consider the use of pre-acquired knowledge which can be useful in improving the quality and speed of motion generation in complex environments. To address the issue, this paper proposes a transfer learning-based evolutionary framework for multi-objective gait optimization, named Tr-GO. The idea is to initialize a high-quality population by using the technique of transfer learning, so any kind of population-based optimization algorithms can be seamlessly integrated into this framework. The advantage is that the generated gait can not only dynamically adapt to different environments and tasks, but also simultaneously satisfy multiple design specifications (e.g., speed, stability). The experimental results show the effectiveness of the proposed framework for the gait optimization problem based on three multi-objective evolutionary algorithms: NSGA-II, RM-MEDA and MOPSO. When transferring the pre-acquired knowledge from the plain terrain to various inclined and rugged ones, the proposed Tr-GO framework accelerates the evolution process by a minimum of 3-4 times compared with non-transferred scenarios.
