Genetic Algorithm-based Strategy Research Articles

Research on position Model of Surface-to-air Missile Defense against Cruise Missile Based on Rule GA

Abstract The deployment of weapon systems prior to the engagement of anti-cruise missiles constitutes the primary research challenge in surface-to-air missile operations. Initially, it is essential to analyze operational requirements and scientifically delineate the guided radar detection area, kill zone, and interception zone based on physical realities. A model should be established to ascertain the fundamental conditions for site deployment, taking into account factors such as position height, shielding angle, and geographic coordinates. In relation to low-altitude approaches by adversarial forces, key considerations include the depth and breadth of firepower from the weapon system in the attack direction, firepower density in that direction, and lethality probability. An Operational Effectiveness model should be developed based on coverage area of firepower, effective interceptions per unit time, and lethality probabilities of weapon systems to evaluate each location’s effectiveness. Finally, leveraging land selection principles and external information coordination mechanisms will facilitate establishing a genetic algorithm-based strategy for deploying surface-to-air missile systems. The validity of this algorithm has been demonstrated through practical examples.

The structure of thin boron nanowires predicted using evolutionary computations

This work describes the implementation of a genetic algorithm-based strategy combined with first-principles computations for identifying the structure of the most stable boron 1D crystals. We focus our attention on the structure of ultrathin 1D boron crystals given the lack of previous experimental and theoretical work on this topic. Our methodology yields low-energy structures for further optimizations at the DFT level with tighter convergence criteria. The simulations involved 1D crystals with up to 8 atoms per unit cell. We have identified four main groups of structures: flat nanowires (monatomic-height stripes) with triangular or triangular and “square” motifs, stripes with larger holes, nanowires with an open tubular shape, and regular nanowires. The diameter-dependent structural changes are discussed.

Genetic algorithm-based strategy for the steam reformer optimization

The steam reforming reaction is widely used for obtaining hydrogen. The reforming reaction has a strong endothermic character, which means it requires a considerable and continuous heat supply to proceed. Due to the process character, a highly non-uniform temperature field develops inside the reactor. It has a consequence in large temperature gradients, leading to the catalyst degradation and a reduced lifetime of the reforming unit. The aim of the presented research is to unify the temperature field developing in the reactor, for easier control of the process and extension of the reformer's life expectancy. A conventional plug-flow reactor consists of a cylindrical pipe body filled with catalyst. The presented methodology included optimizing the catalyst distribution in the reactor to acquire the most uniform temperature field possible. A genetic algorithm is selected as an optimization technique for finding the most advantageous alignment of the catalyst. It is an example of evolutionary algorithms, basing on rules similar to natural selection. The algorithm generates a random, initial population of reactors and the reforming simulation is executed for each of them. The computation results are then evaluated and ranked using predefined fitness functions. The ranked reactors parameters' are further recombined with selection probability based on the fitness values, until a whole new population is created and the algorithm's loop restarts. The higher the fitness value of a specific reactor, the higher are the chances of passing its segments composition to the proceeding generation. The fitness computation leaves a vast space for improvements, as it may be computed based on many different process' parameters. This work focuses on distinguishing differences in the algorithm performance, depending on the formula for fitness calculation. The algorithm's converging speed, overall fitness values of specimens and optimization results were investigated and compared. The results show that the algorithm with an updated fitness calculation procedure performs considerably better. Only about 50% of computational time was required, to acquire results of the same quality for the presented numerical cases, when comparing with the previously prepared procedure.

Mobility modelling for urban traffic surveillance by a team of unmanned aerial vehicles

Use of unmanned aerial vehicles (UAVs) for road traffic surveillance is an exciting idea for improving surveillance quality, as a component of intelligent transportation systems and smart cities. Calibrated mobility models help study and analyse several mobility related issues, for their successful deployment in large urban environments. This paper discusses an energy-aware and scalable mobility model for a team of cooperative UAVs monitoring urban road traffic. It also accompanies an extensible framework for territory distribution, to optimise the number of UAVs required for maximising coverage, without compromising operational performance. Since the problem of territory distribution in general is NP-hard, a genetic algorithm-based strategy, considering edge-disjoint paths, is recommended. Simulation results show that the proposed model considerably improves area coverage in comparison with other mobility models. A published dataset simulating vehicular traffic for a mid-size urban city is used to ascertain scalability to realistic urban territories.

Reducing the Cost of Electricity by Optimizing Real-Time Consumer Planning Using a New Genetic Algorithm-Based Strategy

To ensure the use of energy produced from renewable energy sources, this paper presents a method for consumer planning in the consumer–producer–distributor structure. The proposed planning method is based on the genetic algorithm approach, which solves a cost minimization problem by considering several input parameters. These input parameters are: the consumption for each unit, the time interval in which the unit operates, the maximum value of the electricity produced from renewable sources, and the distribution of energy production per unit of time. A consumer can use the equipment without any planning, in which case he will consume energy supplied by a distributor or energy produced from renewable sources, if it is available at the time he operates the equipment. A consumer who plans his operating interval can use more energy from renewable sources, because the planning is done in the time interval in which the energy produced from renewable sources is available. The effect is that the total cost of energy to the consumer without any planning will be higher than the cost of energy to the consumer with planning, because the energy produced from renewable sources is cheaper than that provided from conventional sources. To be validated, the proposed approach was run on a simulator, and then tested in two real-world case studies targeting domestic and industrial consumers. In both situations, the solution proposed led to a reduction in the total cost of electricity of up to 25%.

Optimal topological balancing strategy for performance optimisation of consensus‐based clock synchronisation protocols in wireless sensor networks: a genetic algorithm‐based approach

Consensus-based clock synchronisation (CCS) protocols have gained recent attention in wireless sensor networks. However, the well-known and state-of-the-art protocols are ‘all node based’, that is, every node iterates the consensus algorithm to reach to the synchronised state by exchanging synchronisation messages with the neighbours. This increases the congestion in the network because of extensive message exchanges and induces packet losses and delay in the network. Hence, it is desirable that a subset of connected sensors along with a balanced number of neighbouring sensors should be selected to form a logical topology which will serve as a virtual backbone for the CCS algorithm. This will minimise the overall message complexity and energy consumption in the network as well as balances and minimises delay for faster consensus convergence with optimal synchronisation error. This problem is claimed to be a generalisation of Load Balanced Connected Dominating Set problem which is recently proved to be NP-complete. To make the problem tractable, a genetic algorithm-based strategy is proposed to select the synchronising nodes to form an optimal logical topology.

Protein-RNA complexes and efficient automatic docking: expanding RosettaDock possibilities.

Protein-RNA complexes provide a wide range of essential functions in the cell. Their atomic experimental structure solving, despite essential to the understanding of these functions, is often difficult and expensive. Docking approaches that have been developed for proteins are often challenging to adapt for RNA because of its inherent flexibility and the structural data available being relatively scarce. In this study we adapted the RosettaDock protocol for protein-RNA complexes both at the nucleotide and atomic levels. Using a genetic algorithm-based strategy, and a non-redundant protein-RNA dataset, we derived a RosettaDock scoring scheme able not only to discriminate but also score efficiently docking decoys. The approach proved to be both efficient and robust for generating and identifying suitable structures when applied to two protein-RNA docking benchmarks in both bound and unbound settings. It also compares well to existing strategies. This is the first approach that currently offers a multi-level optimized scoring approach integrated in a full docking suite, leading the way to adaptive fully flexible strategies.

Sculpting the Analytical Volume in and around Nanoparticle Sensors Using a Multilayer Geometry

The use of structured nanoparticles as optical contrast agents has led to new sensing opportunities in localizing the analytical volume within or outside the particle. Here we examine the use of structured nanoparticles for controlling the sensed analytical volume and figures of merit for their use. Nanolayered alternating metal-dielectric particles (nanoLAMPs), consisting of metal-dielectric nanospheres, are a flexible and highly tunable structure and used here to illustrate the concept of sculpting the analytical volume associated with a nanoparticle. The alternating metal and dielectric shells in LAMPs are designed such that, when illuminated, the plasmonic coupling of metal shells results in amplified electric fields in specific volumes. The strength and extent of regions with amplified fields (hot spots) in and around a LAMP are at the expense of other regions with depleted fields. A rigorous Mie theory formulation is used to model electric field redistributions. A genetic algorithm-based strategy is then employed to design LAMPs that selectively enhance the response of analyte molecules located either outside or in various dielectric layers through electric field redistribution. We demonstrate that it is possible to localize the analytical volume to within or outside the particle quite efficiently. Further, the analytical figures of merit (localization and amplification of signal as well as contrast between sensed species and background) are optimized and limits to the same are described. The strategy proposed here is a general route to engineer a palette of probes with highly specific detection capabilities using spectroscopy techniques based on surface-enhanced scattering, absorption, or emission processes.

Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support

We design a genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. In this approach, an elaborate encoding method is developed, and the relative confidence is used as the fitness function. With genetic algorithm, a global search can be performed and system automation is implemented, because our model does not require the user-specified threshold of minimum support. Furthermore, we expand this strategy to cover quantitative association rule discovery. For efficiency, we design a generalized FP-tree to implement this algorithm. We experimentally evaluate our approach, and demonstrate that our algorithms significantly reduce the computation costs and generate interesting association rules only.

Numerical solutions of the Schrödinger equation directly or perturbatively by a genetic algorithm: test cases

The workability of a genetic algorithm-based strategy to solve the Schrödinger equation directly is tested with reference to a screened Coulomb potential and an oscillator with quartic anharmonicity. The suitability of the same basic idea in solving the inhomogeneous differential equations of Rayleigh–Schrödinger perturbation theory is also examined in this context with particular reference to the ground state of a two-electron atom. Special advantages of the general approach are stressed.