Concertina Gait Learning for Snake Robot using Artificial Neural Network

Wind power ramp events increasingly affect the integration of wind power and cause more and more problems to the safety of power grid operation in recent years. Several forecasting techniques for wind power ramp events have been reported. In this paper, the statistical scenarios forecasting method is proposed for wind power ramp event probabilistic forecasting based on the probability generating model. Multi-objective fitness functions are established considering cumulative density functions and higher order moment autocorrelation functions with respect to the consistency of distribution and timing characteristics, respectively. Parameters of probability generating model are calculated by the iterative optimization using the modified genetic algorithm with multi-objective fitness functions. A number of statistical scenarios captured bands are generated accordingly. Eventually, ramp event probability characteristics are detected from scenarios captured bands to evaluate the ramp event forecasting method. A wind plant of Bonneville Power Administration with actual wind power data is selected for calculation and statistical analysis. It is shown that statistical results with multi-objective functions are more accurate than the results with single objective functions. Moreover, the statistical scenarios forecasting method can accurately estimate the characteristics of wind power ramp events. The results verify that the proposed method can guide the generation method of statistical scenarios and forecasting models for ramp events.

Distributed generations (DGs) have been constantly incorporating into the distribution systems. The optimal planning (sizing and sitting) of the DGs by applying harmony search (HS)-based hybrid genetic algorithm integrated adaptive particle swarm optimization (GA-APSO) technique is exhibited in this work. The fitness evolution function has investigated as the multi-objective function (FMO), which depends on the five significant indexes such as active power loss, reactive power loss, total cost generation index, voltage deviation, and load balancing index. The ideal solution has attained by minimizing the multi-objective (MO) fitness function by deploying HS-based GA-PSO strategy. The projected work will be implemented on IEEE standard 33- and 69-bus radial distribution networks. The performance of the suggested algorithm has analyzed dependent on the five measures, such as active and reactive power loss, voltage profile improvement, reduction in voltage deviation, and cost of operation of generation. The legitimacy of the assessed outcomes has affirmed by comparing with some of the notable optimization algorithms.

This research analyses and discusses the use of Multiobjective fitness function to evolve Finite State Automata. Such automata can describe system’s behavior mathematically in an efficient manner. However system’s behavior must highly depend on its input-output specifications. Genetic Programming is used, and the fitness function is built to guide the evolutionary process in two different cases. First case: Single point fitness function is used where the only focus is on the correctness of the evolved automata. Second case: multiobjective fitness function is used since every real-world problem involves simultaneous optimization of several incommensurable and often competing objectives. Multiobjective optimization is defined as a problem of finding a Finite State Automata which satisfies: parsimony, efficiency, and correctness. It has been presented that for large and complex problems it is necessary to divide them into sub problem(s) and simultaneously breed both sub-program(s) and a calling program.

A new approach to regional land use and transportation planning, which uses a genetic algorithm as an integrated optimization tool, is presented. The approach is illustrated by applying it to the Wasatch Front Metropolitan Region, which consists of four counties in the state of Utah. This genetic algorithm–-based approach was applied earlier to the twin cities of Provo and Orem in Utah, but here it is adapted to regional planning. Three issues make regional planning particularly difficult: ( a) individual cities have significant planning autonomy, ( b) the search space of possible plans is immense, and ( c) preferences between competing objectives vary among stakeholders. The approach used here addresses the first issue by the way the problem is formulated. The second issue is addressed with a genetic algorithm. Such algorithms are particularly well suited to problems with large search spaces. The third issue is addressed by using a multiobjective fitness function in the genetic algorithm. It was found that a genetic algorithm could produce a set of nondominated future land use scenarios and street plans for a region, from which regional planners can make a selection. Execution of the algorithm to produce 100 plans per generation for 100 generations took about 4 days with a high-end personal computer. Interesting trends for reducing change and traffic congestion were discovered.

In this paper, we propose a strategy of obstacle avoidance for a non-holonomic snake robot in a narrow space. In our strategy, the motion of the snake robot is restricted to a gliding curve and the curve is designed such that the snake robot does not come into contact with both obstacles and walls. To generate a desired gliding curve, we use some kind of serpenoid curve which is like a trace of natural snakes and tune its shape. By our strategy, the obstacle avoidance is simplified to designing the gliding curve in two-dimensional field. In this paper, we denote details of our strategy and demonstrate the effectiveness of our strategy by numerical simulations.

Load balancing techniques are a typical NP-hard problem. Currently, many researchers have solved load balancing problem by considering well-known metaheuristic techniques. However, these techniques suffer from one of these issues: premature convergence, poor convergence speed, initially selected random solutions, and stuck in local optima. To handle the issues associated with existing metaheuristic techniques, in this paper, a mutation-based particle swarm optimization based load balancing technique is proposed. The proposed technique has an ability to overcome several issues associated with existing techniques such as premature convergence, poor convergence speed, initially selected random solutions, and stuck in local optima issues. Also, multi-objective fitness function is designed as a minimization problem. Multi-objective fitness function considers energy consumption, makespan, and load imbalance rate parameters. The proposed technique outperforms existing load balancing techniques in terms of makespan, speedup, communication overheads, efficiency, utilization, mean gain time, load imbalance rate, and energy consumption.

This paper deals with the application of gray wolf optimiser (GWO) based on an artificial neural network (ANN) to optimise the coupling of the Kerman combined cycle power plant (KCCPP) with multi-effect distillation desalination (MED) unit. Thermodynamic simulation of the initial power plant has been performed in the GT Pro simulator, and an extracted steam from the steam turbine was used to supply the required energy for the desalination plant. A desalination economic evaluation program called DEEP has been applied to calculate the cost of power and freshwater production. To optimise the system, a multi-objective fitness function has been defined to reach the minimum production cost and maximum performance in the dual-purpose plant. The optimisation procedure has been performed by combining an artificial neural network along with gray wolf optimiser, and the multi-objective fitness function has been predicted by a neural network at each stage of optimisation. In this analysis, the best value of 2.286 has been found for the fitness function by the gray wolf optimiser based on an artificial neural network. This fitness value has been obtained from 89.69 US$/MWh for electric power cost, 40.78% for net electric efficiency, 1.22 US$/m3 for freshwater cost, and 14 for gain output ratio. Highlight Kerman combined cycle power plant with 1912 MWe nominal power has been modelled. A multi-effect distillation desalination plant has been coupled to the power plant to supply 50,000 m3/day of freshwater. To find the optimal design of the dual-purpose plant, an artificial neural network was connected to the gray wolf optimiser algorithm. Multi-objective optimisation has been applied to increase efficiency and reduce costs in the dual-purpose plant.

Earthquakes pose critical challenges for search and rescue (SAR), as narrow operational time windows and hazardous terrain constrain human capabilities – highlighting the need for robotic systems. Biomimetic snake robot systems offer a promising solution with their adaptability and flexibility in navigating irregular surfaces. Prior research has largely focused on mechanical design, with limited quantitative, task-specific evaluation of gaits in post-earthquake settings. To address this gap, we compare lateral undulation and sidewinding gaits, hypothesizing that their differing biomechanics yield distinct advantages across terrains. Using the Bullet Physics engine, we model a snake robot and simulate two SAR-inspired locomotion challenges: (1) a stepped ramp with repeating incline-flat patterns simulating collapsed flooring, and (2) a randomized heightfield terrain resembling concrete rubble. Performance metrics include cost of transport, contact stability, load distribution, and either completion time (Task 1) or success rate (Task 2). Results indicate sidewinding is more energy-efficient across both tasks while lateral undulation provides markedly better contact stability and more consistent load distribution Sidewinding completed Task 1 faster and lateral undulation achieved a higher success rate in Task 2. Additional evaluations showed that directional control parameters held a critical role in improving balance and reducing slippage for both gaits. These findings support informed gait selection in real deployments and advance the development of robust SAR-oriented snake robotics.

Optimally designed multi-material structures offer the potential for increased functionality. The present paper describes a modeling and optimization procedure based on the finite element method (FEM) combined with an evolutionary genetic algorithm (GA). The GA offers the possibility of finding the global optimum in a multi-modal design space. This advantage, however, is counterbalanced by the high computational expense of utilizing many FEM evaluations, as is often required in structural optimization. Furthermore, in the context of multi-material optimization, the large number of material possibilities for each finite element can render the conventional GA-based optimization prohibitively costly in computational time and inconclusive. In this paper, a stepwise mesh refinement technique is presented. Coupled to the GA using a multi-objective fitness function, the method is shown to have a significantly lower computational time and leads to satisfactory designs of heterogeneous objects of arbitrary shapes. Design issues related to the use of this method are discussed and exemplified with the design of a three-dimensional heterogeneous connector.

Calculation of optimal induction heater capacitance based on the smart bacterial foraging algorithm

An optimization-based metasurface is proposed and employed to design a wideband single-layer reflectarray antenna for use in the terahertz (THz) regime. The random hill-climbing optimization method is utilized to obtain a wideband unit cell. A multiobjective fitness function is defined to consider three required design aims, whereas an established link between MATLAB and HFSS software is employed to simulate the required periodic metasurface. The final created unit cell has a remarkable bandwidth of 44.89% together with −0.44 dB average variation of the magnitude of the reflection coefficient. Moreover, the phase variation versus various scales of the metallic surface of the cell is more than 300° at all of the desired frequencies between 0.95 THz and 1.5 THz. Two different reflectarray antennas are proposed based on the optimized cell, and two excitation scenarios are implemented to investigate the performance of the designed reflectarray antennas. In the first scenario, the metasurface is designed to deflect a plane wave with angle of incidence of $$30^{^\circ }$$ to the normal direction. In the second one, a THz feeding horn illuminates a metasurface reflector including 201 elements. According to the simulation results, 3-dB and 1-dB gain bandwidths of 39.67% (0.99–1.48 THz) and 20.51% (1.05–1.29 THz), respectively, are achieved, whereas the maximum gain is 19.8 dBi at 1.2 THz.

Many state-of-the-art approaches to power minimisation in Networks-on-Chip (NoC) are based on the reduction of the communication paths taken by packets over the interconnect. This is often done by optimising the packet routing, the allocation of tasks that produce and consume those packets, or both. In all cases, the optimisation affects the timeliness of the packets, because changes will occur in the way resources are shared at the platform cores (as tasks are reallocated) and NoC links (as packet routes are changed). In this paper, we propose an optimisation technique that is able to minimise power dissipation without sacrificing timing constraints, thus suitable to systems with hard real-time requirements. It is based on a Genetic Algorithm (GA) that evolves chromosomes representing the mapping of tasks to cores, guided by a multi-objective fitness function that combines power estimation macromodels and real-time schedulability analysis.

Most of the generated electricity is lost in power loss while transmitting and distributing it to the consumer end. The power losses occurring in the distribution network cause deviation in voltage and lower stability due to increased load demand. The integration of multiple Distributed Generation (DG) will enable the existing radial electrical distribution network efficient by minimizing the power losses and improving the voltage profile. Metaheuristic optimization techniques provide a favorable solution for optimal location and sizing of DG in the distribution network. A novel modern metaheuristic Transient Search Optimization (TSO) algorithm, inspired by the electrical network’s transient response of storage components implemented in the proposed work. The TSO formulated optimal DGs allocation to minimize total active power loss, voltage deviation and enhance voltage stability index as minimization optimization problem satisfying various equality and inequality constraints. The installation of multiple DG units at unity, fixed, and optimal power factors were examined. The TSO algorithm’s effectiveness was tested on standard IEEE 33-bus and 69-bus radial distribution networks, including various operating events developed in the form of single and multi-objective fitness functions. The active power loss reduced to 94.29 and 94.71% for IEEE 33 and 69 bus distribution systems. The obtained results trustworthiness is confirmed by comparison with well-known optimization methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Algorithm (TLBO), Swine influenza model-based optimization with quarantine (SIMBO-Q), Multi-Objective Harris Hawks optimizer (MOHHO) and other provided in the literature. The presented numerical studies represent the usefulness and out-performance of the proposed TSO algorithm due to its exploration and exploitation optimization mechanisms for the DG allocation problem meticulously.

A fractured reservoir in Bongor Basin has been in production by natural energy for almost a decade. However, its production performance has deteriorated over time, as evidenced by the declining pressure levels, a significant drop in oil production rates, and a rapid increase in water cut in the edge part. To enhance the reservoir’s productivity, this paper aims to characterize the flow pattern under water and gas flooding to clarify the mechanisms of energy recovery, and optimize injection-production parameters for field application. In this paper, development effect of the reservoir is comprehensively evaluated, with a particular focus on understanding the key production performance features. The study considers subsurface fracture distributions and employs a two-dimensional visualization model to investigate the flow characteristics of water and gas in fracture and matrix paths. Furthermore, discrete fractured models are employed to simulate water and gas flooding, followed by sensitive analysis and history match to reveal the influence of fracture on production performance. The simulations, coupled with improved genetic algorithm, create optimal production schemes that can be directly applied in the field. The results indicate that as depletion progresses, pressure levels and oil rates in the areas with developed fractures decline apparently. Fractures have distinct roles in production performance and are essential to improve development. The visualization model reveals the sweep characteristics of water and gas flooding in various fracture distributions. It is evident that water flows preferentially through extensive fracture networks to extend overall sweep but with lower displacement efficiency, while gas can access thinner fractures and even certain matrix regions. The sweep and displacement of water and gas are quantified using standardized curve methods. Core flooding experiments demonstrate that water and gas flooding can extract additional oil after depletion, while improper injection can result in channeling or lower recovery. Based on the collaborations of water and gas flooding, the controlling factors on flow characterization under various fracture distribution and development conditions are screened using discrete fracture models, indicating fracture density, azimuth and production parameter combination posing evident influence. By using a multi-objective fitness function, the traditional genetic algorithm is improved, and in combination with models, the injection volume, timing, and mode are optimized, resulting in the optimal development parameter combinations. The field has implemented injection of water in edge and gas in top, which has appeared favorable signs in production. This study demonstrates the advancement of using visualization experiments to in depth analyze the flow mechanism in fractured reservoirs, and highlights its innovation by optimizing development schemes using an improved genetic algorithm, which significantly enhances work efficiency. It provides valuable insights for the design and operation of water and gas injection in the reservoirs.

Recently, compressive sensing theory has opened up a new path for the development of signal processing. According to this theory, a novel single pixel camera system has been introduced to overcome the current limitation and challenges of manufacturing large scale photo sensor arrays. In the system, image can be recovered from original image using random measurements by means of compressive sensing techniques. In the image recovering process, some default parameters are used. It is important to find optimizing to enhance image accuracy and recovering speed. Some images recovering algorithms were attempted to recover images and some parameters of the algorithms need be set appropriately during the recovering process. In order to find the better values of the parameters, in this paper, a multi-objective optimizing method is proposed. Accuracy and rapidity are selected for optimization goal, and a multi-objective fitness function is built. Then some optimization parameters are selected and the ranges of the parameters are decided. And genetic algorithm is used in the optimization process. Finally, the result of optimization is used in a single pixel camera system. And the results of recovering images are better than the default parameter's for a single pixel camera experiment.