Crayfish Optimization Algorithm

Backtracking Search Optimization Algorithm for numerical optimization problems

Smart grid is an emerging technology which is successfully implemented by the use of different communication methods. Demand side management (DSM) plays a significant role in the management of load and energy consumption in order to reduce cost in the smart grids. Smart buildings and smart homes are usually considered important for reducing the electricity consumption by home energy management controllers (HEMC). In this paper, an energy management controller (EMC) is proposed with an objective to minimize the energy consumption, curtail load demand, reduce electricity usage and minimize peak to average (PAR) ratio with increased user comfort level. For this purpose, three evolutionary and iterative techniques were proposed as backtracking search optimization algorithm (BSOA), tabu search (TS) and their hybrid as tabu backtracking search optimization (TBSO) algorithm. Simulations were performed in MATLAB by using real time pricing (RTP) tariff for bill calculation. 9 household appliances were scheduled and results proved that our proposed hybrid technique outperforms the remaining two in terms of cost minimization.

Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Common spatial pattern (CSP) is a powerful algorithm for extracting discriminative brain patterns in motor imagery-based brain-computer interfaces (BCIs). However, its performance depends largely on the subject-specific frequency band and time segment. Accurate selection of most responsive frequency band and time segment remains a crucial problem. A novel evolutionary algorithm, the backtracking search optimization algorithm is used to find the optimal frequency band and the optimal combination of frequency band and time segment. The former is searched by a frequency window with changing width of which starting and ending points are selected by the backtracking optimization algorithm; the latter is searched by the same frequency window and an additional time window with fixed width. The three parameters, the starting and ending points of frequency window and the starting point of time window, are jointly optimized by the backtracking search optimization algorithm. Based on the chosen frequency band and fixed or chosen time segment, the same feature extraction is conducted by CSP and subsequent classification is carried out by Fisher discriminant analysis. The classification error rate is used as the objective function of the backtracking search optimization algorithm. The two methods, named BSA-F CSP and BSA-FT CSP, were evaluated on data set of BCI competition and compared with traditional wideband (8-30[Formula: see text]Hz) CSP. The classification results showed that backtracking search optimization algorithm can find much effective frequency band for EEG preprocessing compared to traditional broadband, substantially enhancing CSP performance in terms of classification accuracy. On the other hand, the backtracking search optimization algorithm for joint selection of frequency band and time segment can find their optimal combination, and thus can further improve classification rates.

As a young intelligence optimization algorithm, backtracking search optimization algorithm (BSA) has been used to solve many optimization problems successfully. However, BSA has some disadvantages such as being easy to fall into local optimum, lacking the learning from the optimal individual, and being difficult to adjust the control parameter F. Motivated by these analyses, to improve the optimization performance of the original BSA, a new hybrid hierarchical backtracking search optimization algorithm (HHBSA) is proposed in this paper. In the proposed method, a two-layer hierarchy structure of population and a randomized regrouping strategy are introduced in the proposed HHBSA for improving the diversity of population, a mutation strategy is used to help the population when the evolution is stagnant and an adaptive control parameter is presented to increase the learning ability of the BSA. To verify the performance of the proposed approaches, 48 benchmark functions and three real-world optimization problems are evaluated to test the performance of the proposed approach. Experiment results indicate that HHBSA is competitive to some existing EAs.

This paper presents a Charged System Search (CSS) algorithm to solve for robotic drill path optimization involved in printed circuit board (PCB) manufacturing industries. Most of the operational time of a PCB Robotic Drill is spent on moving the drill bit between the holes. An optimized path translates to a minimal cost of operating the robot. The drill path consists of a number of potential locations where the holes are going to be drilled. As the number of holes required increases so thus does the complexity to find the optimized path. Recently developed CSS algorithm proposed to solve this complex problem with minimal computational time. The performance of CSS algorithm is tested and verified with four case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable to efficiently find the optimal path for PCB holes drilling process with reasonable computational time.

The estimation of low and high-pass active filter parameters with opposite charged system search algorithm

The dynamic voltage restorer's (DVR) transient, steady-state, and dynamic responses are essential requirements for protecting sensitive loads against upstream voltage disturbances via the DVR's ride-through capabilities. DVRs also look after transient oscillation at the instant of entering, and /or exiting by the DVR. This paper presents an enhanced, optimized, and less complex DVR control system structure, which is capable of improving the transient, steady-state, and dynamic responses as well as eliminating inherent transient oscillations. The control system comprises a closed-loop feedback control signal and feedforward upstream disturbance detection signal. Incorporating the feedforward term helps, dramatically, in improving the system response and eliminating the transient oscillations in the load voltage. The error signals are adapted using a PI controller to make the load voltage faithfully track its predefined reference waveform. The controller is implemented in the dq synchronous rotating reference frame. The parameters of the PI controller are selected using modern population-based optimization called the Harris Hawks Optimization (HHO) technique. The results obtained using the HHO technique are compared with two other optimization algorithms, namely Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). The results show that the HHO gives the best system response. The system is simulated using MATLAB/Simulink and the validation via Typhoon HIL402 real-time emulator. Both HIL402 validation and simulation results show that the proposed control scheme recovers normal operation against voltage disturbance within approximately 1.2 milliseconds without overshoot with steady-state error near zero and significantly dampens the inherent voltage oscillation that occurs at the instant of DVR entrance and/or exit.

Survival exploration strategies for Harris Hawks Optimizer

The ambulance routing problem is one of the capacitated ambulance routing problem forms. It deals with injuries and their requests for saving. Therefore, the main aim of the ambulance routing problem is to determine the minimum (i.e., optimum) required distances of between: 1) accident places and the ambulance station; 2) the location of the nearest hospital and the accident places. Although of the efforts proposed in the literature, determining the optimum route is crucial. Therefore, this article seeks to tackle ambulance vehicle routing in smart cities using Harris Hawks Optimization (HHO) algorithm. It attempts to take the victims as quickly as possible and confidently. Several engineering optimization problems confirm that HHO outperforms many well-known Swarm intelligence approaches. In our system, let’s use the node approach to produce a city map. Initially, the control station receives accident site information and sends it to the hospital and the ambulance. The HHO vehicle routing algorithm receives data from the driver; the data includes the location of the accident and the node position of the ambulance vehicle. Then, the driver’s shortest route to the accident scene by the HHO. The locations of the accident and hospital are updated by the driver once the car reaches the accident site. The fastest route (which results in the least travel time) to the hospital is then determined. The HHO can provide offline information for a potential combination of the coordinates of destination and source. Extensive simulation experiments demonstrated that the HHO can provide optimal solutions. Furthermore, performance evaluation experiments demonstrated the superiority of the HHO algorithm over its counterparts (SAODV, TVR, and TBM methods). Furthermore, for ten malicious nodes, the PDF of the algorithm was 0.91, which is higher than the counterparts

The aim of this study was to predict Bitcoin prices by employing machine learning algorithms in combination with the HHO (Harris Hawks Optimization) and AO (Aquila Optimizer) optimization algorithms. In this study, the most influential variables affecting Bitcoin price were first identified using the meta-synthesis method. Subsequently, the proposed model, which integrates CNN and LSTM architectures, was designed and implemented to analyze complex patterns and temporal dependencies. To enhance prediction accuracy and optimize parameter tuning, the Harris Hawks Optimization (HHO) and Aquila Optimization (AO) algorithms were employed. The obtained results demonstrate that the proposed hybrid model outperforms previous methods in terms of prediction accuracy (based on indicators such as RMSE and MAPE) and adaptability to market volatility. In the present study, the proposed hybrid model exhibited superior performance compared to prior methods in terms of prediction accuracy (measured by metrics such as RMSE and MAPE) and flexibility in coping with market fluctuations. This model can assist investors, market analysts, and financial policymakers in risk management, informed decision-making, and optimizing investment strategies.

A learning and niching based backtracking search optimisation algorithm and its applications in global optimisation and ANN training

An efficient hybrid sine-cosine Harris hawks optimization for low and high-dimensional feature selection

Backtracking search optimization algorithm with dual scatter search strategy for automated test case generation

A considerable number of intermittent renewable resources like photovoltaic generation and wind energy when integrated into the conventional grid technology causes serious issues in the power systems like frequency instability. So an intelligent controller is desired for steady and reliable operation of the electric grid. This paper deals with the frequency control of hybrid power system (HPS) employing a sine cosine adopted Harris' hawks optimization (SCaHHO) technique. The proposed technique aims at improving the performance of the original Harris' hawks optimization (HHO) algorithm by incorporating a sine and cosine function in the calculation of escaping energy and chances of escaping of prey respectively to emphasize the exploitation process of hawks. The effectiveness of the SCaHHO technique is validated with the novel HHO technique as well as moth flame optimization, sine cosine algorithm, grey wolf optimization, salp swarm algorithm and gravitational search algorithm using 23 standard test functions. A non-parametric statistical investigation is also carried out to verify the effectiveness of the suggested technique. Later the SCaHHO technique is utilized to tune a newly proposed adaptive fuzzy proportional integral derivative controller (AFPID) for frequency control of HPS. The suggested SCaHHO-tuned AFPID controller achieves an improved control action by moderating the frequency fluctuations as compared to the PID controller. Hardware-in-loop validation of the proposed load frequency control scheme is also carried out using OPAL-RT to measure its fidelity with the numerical simulation results.