Optical logic gates play a crucial role in all-optical signal processing systems. Traditional methods of designing logic gates require manual adjustment of structural parameters. In this paper, we ...utilize a genetic algorithm for inverse design, and the optical AND, OR, and NOT logic gates are achieved on a silicon platform at the working wavelength of 1.55 μm. The total area of the logic gates is fixed at 2.2 μm × 2.2 μm, convenient to be integrated with other functional devices, the optimized structural parameters are acquired for different logic gates and the contrast ratios of the OR, AND, and NOT gates are 8.55, 5.32, and 4.14 dB, respectively. The design is characterized by a compact structure, high contrast, and a high degree of freedom, offering a valuable reference for photonic integrated circuits.
•A compact optical logic gate with rectangular air hole array is designed and high performance is achieved.•The optimization efficiency was enhanced by GA, and optical logic gates were achieved with ultra-small size.•The influence of air hole’s variation to device’s performance was studied, and it guided the actual fabrication.
•Multiple attributes from IP flows are combined to detect anomalous events.•GA metaheuristic used for Digital Signature of Network Segment using Flow Analysis.•Unsupervised training technique applied ...efficiently for network traffic profiling.•Fuzzy Logic improved accuracy and false positives compared to state of art.
Due to the sheer number of applications that uses computer networks, in which some are crucial to users and enterprises, network management is essential. Therefore, integrity and availability of computer networks become priorities, making it a fundamental resource to be managed. In this work, a scheme combining Genetic Algorithm and a Fuzzy Logic for network anomaly detection is discussed. The Genetic Algorithm is used to generate a Digital Signature of Network Segment using Flow Analysis, where information extracted from network flows data is used to predict the networks traffic behavior for a given time interval. Furthermore, a Fuzzy Logic scheme is applied to decide whether an instance represents an anomaly or not, differing from some approaches present in the literature. Indeed, it is proposed an expert system with the capability to monitor the network’s traffic with IP flows while expected behaviors are generated in a regular time interval basis, issuing alarms when a possible problem is present. The proposed anomaly detection system exposes network problems autonomously. The results acquired from applying the proposed approach in a real network traffic flows achieve an accuracy of 96.53% and false positive rate of 0.56%. Moreover, our method succeeds in achieving higher performance compared to several other approaches.
A runtime analysis of the Simple Genetic Algorithm (SGA) for the OneMax problem has recently been presented proving that the algorithm with population size μ≤n1/8−ε requires exponential time with ...overwhelming probability. This paper presents an improved analysis which overcomes some limitations of the previous one. Firstly, the new result holds for population sizes up to μ≤n1/4−ε which is an improvement up to a power of 2 larger. Secondly, we present a technique to bound the diversity of the population that does not require a bound on its bandwidth. Apart from allowing a stronger result, we believe this is a major improvement towards the reusability of the techniques in future systematic analyses of GAs. Finally, we consider the more natural SGA using selection with replacement rather than without replacement although the results hold for both algorithmic versions. Experiments are presented to explore the limits of the new and previous mathematical techniques.
•A logistics distribution region partitioning model is developed.•This model is to minimize the cost of two-echelon logistics distribution network.•A hybrid algorithm with PSO and GA is proposed.•The ...empirical results reveal that EPSO–GA algorithm outperforms other algorithms.
Two-echelon logistics distribution region partitioning is a critical step to optimize two or multi-echelon logistics distribution network, and it aims to assign distribution unit to a certain logistics facility (i.e. logistic center and distribution center). Given the partitioned regions, vehicle routing problem can be further developed and solved. This paper established a model to minimize the total cost of the two-echelon logistics distribution network. A hybrid algorithm named as the Extended Particle Swarm Optimization and Genetic Algorithm (EPSO–GA) is proposed to tackle the model formulation. A two-dimensional particle encoding method is adopted to generate the initial population of particles. EPSO–GA combines the merits of Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) with both global and local search capability. By updating the inertia weight and exchanging best-fit solutions and worst-fit solutions between PSO and GA, EPSO–GA algorithm is able to converge to an optimal solution with a reasonable design of termination and iteration rules. The computation results from a case study in Guiyang city, China, reveal that EPSO–GA algorithm is superior to the other three algorithms, Hybrid Particle Swarm Optimization (HPSO), GA, and Ant Colony Optimization (ACO), in terms of the partitioning schemes, the total cost and number of iterations. By comparing with the exact method, the proposed approach demonstrates its capability to optimize a small scale two-echelon logistics distribution network. The proposed approach can be readily implemented in practice to assist the logistics operators reduce operational costs and improve customer service. In addition, the proposed approach is of great potential to apply in other research domains.
This letter presents a novel approach to constructing 1-bit coding unit cells for metasurface design and utilizes a genetic algorithm (GA) to effectively reduce the radar cross section (RCS) of the ...antenna over a wide frequency range. Unlike previous methods, this approach allows for manipulation of the coding state of unit cells by modifying the sheet impedance of resistive graphene rather than altering conventional metallic structures. The 1-bit coding units "0" and "1" can be achieved by utilizing graphene Rs values of 10 and 1000 Ω/sq, respectively, resulting in a reflection response with a phase difference of π and relatively large amplitude. The measurement results were compared with full-wave simulation ones to benchmark the performance of the proposed antenna. The antenna achieves more than 10 dB RCS reduction band from 12.5-27.7 GHz with a fractional bandwidth of 75.6%. The proposed design exhibits promising potential applications in stealth technology for intricate entities.
In smart grids, one of the most important objectives is the ability to improve the grid's situational awareness and allow for fast-acting changes in power generation. In such systems, an energy ...management system should gather all the needed information, solve an optimization problem, and communicate back to each distributed energy resource (DER) its correct allocation of energy. This paper proposes a memory-based genetic algorithm (MGA) that optimally shares the power generation task among a number of DERs. The MGA is utilized for minimization of the energy production cost in the smart grid framework. It shares optimally the power generation in a microgrid including wind plants, photovoltaic plants, and a combined heat and power system. In order to evaluate the performance of the proposed approach, the results obtained by the MGA are compared with the results found by a genetic algorithm and two variants of particle swarm optimization. Simulation results accentuate the superiority of the proposed MGA technique.
Autonomous pilot is crucial in integrally promoting the autonomy of an unmanned surface vehicle (USV). However, the integration mechanism of decision and control is still unclear within the entire ...autonomy. In this paper, by organically bridging path planning and tracking, an autonomous pilot framework with waypoints generation, path smoothing and policy guidance of a USV in congested waters is established, for the first time. Incorporating elite and diversity operations into the genetic algorithm (GA), an elite-duplication GA (EGA) strategy is devised to optimally generate sparse waypoints in a constrained space. The B-spline technique is further deployed to make flexibly smooth interpolation facilitating path smoothing supported by optimal sparse-waypoints. Seamlessly bridged by the parametric smooth path, deep reinforcement learning (DRL) technique is resorted to continuously extract in-depth pilotage policies, i.e., mappings from path tracking errors, collision risks and control constraints to continuous control forces/torques. Eventually, the entire spline-bridged EGA-DRL (SED) framework merits autonomous global-pilotage and local-reaction in an organically modular manner. Comprehensive validations and comparisons in various real-world geographies demonstrate the effectiveness and superiority of the proposed SED autonomous pilot framework.
To overcome the disadvantages of traditional genetic algorithms, which easily fall to local optima, this paper proposes a hybrid genetic algorithm based on information entropy and game theory. First, ...a calculation of the species diversity of the initial population is conducted according to the information entropy by combining parallel genetic algorithms, including using the standard genetic algorithm (SGA), partial genetic algorithm (PGA) and syncretic hybrid genetic algorithm based on both SGA and PGA for evolutionary operations. Furthermore, with parallel nodes, complete-information game operations are implemented to achieve an optimum for the entire population based on the values of both the information entropy and the fitness of each subgroup population. Additionally, the Rosenbrock, Rastrigin and Schaffer functions are introduced to analyse the performance of different algorithms. The results show that compared with traditional genetic algorithms, the proposed algorithm performs better, with higher optimization ability, solution accuracy, and stability and a superior convergence rate.
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