Krill Herd Algorithm Research Articles

Improved krill swarm algorithm and its application in structural optimization

This paper improves and perfects the standard Krill herd algorithm (KH), which has the defects of slow convergence speed, insufficient calculation accuracy and easy to fall into local optimal solution for complex problems. An improved Krill herd algorithm (SDEKH) integrating improved differential evolution operator and S-type adaptive inertia weight is proposed. SDEKH, KH and other intelligent algorithms are compared and tested through a variety of standard test functions to verify the excellent performance of SDEKH; SDEKH is used to optimize the design of truss structure. By comparing the optimization results with other methods, it is verified that the optimization efficiency and accuracy of SDEKH are improved, which provides a more efficient and accurate method for the optimization design of engineering structures.

Multi-Objective Optimization of Resilient, Sustainable, and Safe Urban Bus Routes for Tourism Promotion Using a Hybrid Reinforcement Learning Algorithm

Urban transportation systems in tourism-centric cities face challenges from rapid urbanization and population growth. Efficient, resilient, and sustainable bus route optimization is essential to ensure reliable service, minimize environmental impact, and maintain safety standards. This study presents a novel Hybrid Reinforcement Learning-Variable Neighborhood Strategy Adaptive Search (H-RL-VaNSAS) algorithm for multi-objective urban bus route optimization. Our mathematical model maximizes resilience, sustainability, tourist satisfaction, and accessibility while minimizing total travel distance. H-RL-VaNSAS is evaluated against leading optimization methods, including the Crested Porcupine Optimizer (CPO), Krill Herd Algorithm (KHA), and Salp Swarm Algorithm (SSA). Using metrics such as Hypervolume and the Average Ratio of Pareto Optimal Solutions, H-RL-VaNSAS demonstrates superior performance. Specifically, H-RL-VaNSAS achieved the highest resilience index (550), sustainability index (370), safety score (480), tourist preferences score (300), and accessibility score (2300), while minimizing total travel distance to 950 km. Compared to other methods, H-RL-VaNSAS improved resilience by 12.24–17.02%, sustainability by 5.71–12.12%, safety by 4.35–9.09%, tourist preferences by 7.14–13.21%, accessibility by 4.55–9.52%, and reduced travel distance by 9.52–17.39%. This research offers a framework for designing efficient, resilient, and sustainable public transit systems that align with urban planning and transportation goals. The integration of reinforcement learning with VaNSAS significantly enhances optimization capabilities, providing a valuable tool for mathematical and urban transportation research communities.

Machine learning optimization for hybrid electric vehicle charging in renewable microgrids

Renewable microgrids enhance security, reliability, and power quality in power systems by integrating solar and wind sources, reducing greenhouse gas emissions. This paper proposes a machine learning approach, leveraging Gaussian Process (GP) and Krill Herd Algorithm (KHA), for energy management in renewable microgrids with a reconfigurable structure based on remote switching of tie and sectionalizing. The method utilizes Gaussian Process (GP) for modeling hybrid electric vehicle (HEV) charging demand. To counteract HEV charging effects, two scenarios are explored: coordinated and intelligent charging. A novel optimization method inspired by the Krill Herd Algorithm (KHA) is introduced for the complex problem, along with a self-adaptive modification to tailor solutions to specific situations. Simulation on an IEEE microgrid demonstrates efficiency in both scenarios. The predictive model yields a remarkably low Mean Absolute Percentage Error (MAPE) of 1.02381 for total HEV charging demand. Results also reveal a reduction in microgrid operation cost in the intelligent charging scenario compared to coordinated charging.

Feature Selection Using Hybrid Metaheuristic Algorithm for Email Spam Detection

Abstract In the present study, Krill Herd (KH) is proposed as a Feature Selection tool to detect spam email problems. This works by assessing the accuracy and performance of classifiers and minimizing the number of features. Krill Herd is a relatively new technique based on the herding behavior of small crustaceans called krill. This technique has been combined with a local search algorithm called Tabu Search (TS) and has been successfully employed to identify spam emails. This method has also generated much better results than other hybrid algorithm optimization systems such as the hybrid Water Cycle Algorithm with Simulated Annealing (WCASA). To assess the effectiveness of KH algorithms, SVM classifiers, and seven benchmark email datasets were used. The findings indicate that KHTS is much more accurate in detecting spam mail (97.8%) than WCASA.

Sizing and Shape Optimization of Discrete Truss Employing a Target-Oriented Krill Herd Algorithm

Abstract The krill herd (KH) algorithm is widely used for optimizing truss structures as no gradient information is necessary, and only a few parameters require adjustment. However, when the truss structure becomes discrete and complex, KH tends to fall into a local optimum. Therefore, a novel target-oriented KH (TOKH) algorithm is proposed in this study to optimize the design of discrete truss structures. Initially, a crossover operator is established between the “best krill” and “suboptimal krill” to generate a robust “cross krill” for global exploration. Additionally, an improved local mutation and crossover (ILMC) operator is introduced to fine-tune the “center of food” and candidate solutions for local exploitation. The proposed method and other optimization approaches are experimentally compared considering 15 benchmark functions. Then, the performance of the TOKH algorithm is evaluated based on four discrete truss structure optimization problems under multiple loading conditions. The obtained optimization results indicate that the proposed method presents competitive solutions in terms of accuracy, unlike other algorithms in the literature, and avoids falling into a local minimum.

Smart landscaping design for sustainable net-zero energy smart cities: Modeling energy hub in digital twin

In our study, we introduce an innovative energy management system optimized for energy hubs in net-zero energy smart cities, emphasizing sustainability as a core objective. Our approach intricately integrates distributed generation systems and storage facilities within a digital twin framework, incorporating landscape considerations to promote environmental harmony. By prioritizing sustainability, our strategy aims to minimize operational costs while maximizing energy efficiency and reducing carbon emissions. We employ specialized energy flow algorithms for each grid, designing energy hubs that leverage renewable resources and mobile storage to enhance sustainability. To address complexity, our model utilizes a non-convex mixed-integer nonlinear programming structure and robust adaptive optimization techniques, combining the Gray Wolf and Krill Herd algorithms. Through simulations, we demonstrate significant improvements in power system efficiency, including notable reductions in power loss, peak voltage fluctuations, and temperature variations. Our research underscores the critical role of smart landscaping in advancing sustainable energy management for net-zero energy smart cities, facilitating seamless integration of energy hubs with the surrounding landscape within a digital twin paradigm.

A cooperative and competitive krill herd algorithm for structural optimization

This article proposes a novel method, named the cooperative and competitive krill herd (CCKH) algorithm, in which both cooperative strategy and competitive strategy are introduced to solve the structural optimization. In the cooperative strategy, a crossover operator is established between the ‘best krill’ and ‘food’ to generate ‘cooperative krill’, which facilitates a balance between global exploration and local exploitation. In the competitive strategy, krill individuals with poor fitness are replaced with robust ‘competitive krill’ to accelerate global convergence. Then, the experimental results from CEC2017 benchmark functions demonstrate that, compared with other algorithms, CCKH shows superiority in most cases. Finally, six structural optimizations are used to evaluate the robustness of CCKH compared with other techniques. It is found that as the complexity of the structure increases, the competitiveness of CCKH becomes more pronounced, and the time complexity of the presented method is not affected by the two strategies.

Why it does not work? Metaheuristic task allocation approaches in Fog-enabled Internet of Drones

Several scenarios that use the Internet of Drones (IoD) networks require a Fog paradigm, where the Fog devices, provide time-sensitive functionality such as task allocation, scheduling, and resource optimization. The problem of efficient task allocation/scheduling is critical for optimizing Fog-enabled Internet of Drones performance. In recent years, many articles have employed meta-heuristic approaches for task scheduling/allocation in Fog-enabled IoT-based scenarios, focusing on network usage and delay, but neglecting execution time. While promising in the academic area, metaheuristic have many limitations in real-time environments due to their high execution time, resource-intensive nature, increased time complexity, and inherent uncertainty in achieving optimal solutions, as supported by empirical studies, case studies, and benchmarking data. We propose a task allocation method named F-DTA that is used as the fitness function of two metaheuristic approaches: Particle Swarm Optimization (PSO) and The Krill Herd Algorithm (KHA). We compare our proposed method by simulation using the iFogSim2 simulator, keeping all the settings the same for a fair evaluation and only focus on the execution time. The results confirm its superior performance in execution time, compared to the metaheuristics.

Forecasting solar irradiation using convolutional long short-term memory and feature selection of data from neighboring locations

The capacity of renewable energy systems, such as photovoltaic farms, has been steadily increasing owing to the continuous depletion of fossil-based energy sources. Information that can be used to optimize the use of renewable energy is required. This work proposes a novel method that uses data from neighboring locations to perform 24 h-ahead solar GHI forecasting. The approach is helpful in supporting the operation of photovoltaic farms, minimizing the need to collect new data on the target location. This work proposes the Convolutional Long Short-term Memory (ConvLSTM) model to maximize the performance using a feature-rich spatiotemporal dataset. The ConvLSTM model uses Hadamard convolutions in its input-to-state transition and state-to-state transition, allowing it to retain better information. The feature selection methods are used to ensure that only the critical subsets of the dataset are used to develop the model. The subsets identified by the feature selection methods are used on Vector Autoregressive, Support Vector Regression, Multilayer Perceptron, Convolutional Neural Network (CNN), CNN-LSTM and ConvLSTM networks. The winning method is compared to related studies using Stacked-LSTM with transfer-learning, attention-based LSTM and Krill-Herd Algorithm – Support vector Regression (KHA-SVR). The resulting coefficients of determination and root-mean-squared errors were statistically evaluated and compared. Ultimately, the proposed ConvLSTM model performed consistently better across all the seasons and feature subsets.

Optimizing microgrid performance: Strategic integration of electric vehicle charging with renewable energy and storage systems for total operation cost and emissions minimization.

At present, renewable energy sources (RESs) and electric vehicles (EVs) are presented as viable solutions to reduce operation costs and lessen the negative environmental effects of microgrids (μGs). Thus, the rising demand for EV charging and storage systems coupled with the growing penetration of various RESs has generated new obstacles to the efficient operation and administration of these μGs. In this regard, this paper introduces a multi-objective optimization model for minimizing the total operation cost of the μG and its emissions, considering the effect of battery storage system (BSS) and EV charging station load. A day-ahead scheduling model is proposed for optimal energy management (EM) of the μG investigated, which comprises photovoltaics (PVs), fuel cells (FCs), wind turbines (WTs), BSSs, and EV charging stations, with shed light on the viability and benefits of connecting BSS with EV charging stations in the μG. Analyzing three case studies depending on the objective function-Case 1: execute EM to minimize total operation cost and maximize the profits of BSS, Case 2: execute EM to minimize total emission from the μG, and Case 3: execute EM to minimize total operation cost, maximize the profits of BSS, and minimize total emissions from the μG. The main aim of the presented optimization strategy is to achieve the best possible balance between reducing expenses and lessening the environmental impact of greenhouse gas emissions. The krill herd algorithm (KHA) is used to find the optimal solutions while considering various nonlinear constraints. To demonstrate the validity and effectiveness of the proposed solution, the study utilizes the KHA and compares the obtained results with those achieved by other optimization methods. It was demonstrated that such integration significantly enhances the μG's operational efficiency, reduces operating costs, and minimizes environmental impact. The findings underscore the viability of combining EV charging infrastructure with renewable energy to meet the increasing energy demand sustainably. The novelty of this work lies in its multi-objective optimization approach, the integration of EV charging and BSS in μGs, the comparison with other optimization methods, and the emphasis on sustainability and addressing energy demand through the utilization of renewable energy and EVs.

Krill herd technique for dynamic economic dispatch problems with the integration of wind power generation

The krill herd (KH) approach is proposed in this article for tackling the dynamic economic dispatch (DED) problem with the integration of wind power generation, including constraints such as valve-point loadings and prohibited operating zones (PoZs). The purpose of resolving the DED problem is to determine the optimum feasible combination of power outputs for all allocated generating units within a given period while simultaneously meeting dynamic operational constraints and power demand. Due to the addition of valve points and PoZs to the cost characteristics of the generating unit, the DED issue becomes a highly non-linear and non-convex optimization issue. When transmission losses are taken into account, the DED problem becomes significantly more complex to solve. KH is a swarm-inspired algorithm based on krill individual herding behavior. KH employs two global and two local tactics, and these work in parallel, making KH a powerful algorithm. The effectiveness of the KH method is investigated and validated through extensive testing on various test systems, including 5-, 6-, 10-, 16-, and 30-unit test systems. Extensive studies are conducted to evaluate the efficacy of the proposed KH algorithm; the present research work compares the convergence properties of the suggested approach with those of the other recently published methods. In addition to reduced generation costs compared to other results published in recent literature, the numerical findings of the KH approach reveal that it has strong convergence qualities.

Solution of Reactive Power Planning with TCSC and UPFC Using Improved Krill Herd Algorithm

Solution of Reactive Power Planning with TCSC and UPFC Using Improved Krill Herd Algorithm

A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length

A smartphone equipped with a Global Navigation Satellite System (GNSS) module can generate positional information for location-based services. However, GNSS signals are susceptible to fragility, multipath (MP), and Non-Line-Of-Sight (NLOS) interference, which can lead to a degradation in the accuracy of GNSS positioning on smartphones. Due to limitations in the smartphone’s antenna, GNSS signal strength is typically lower. Moreover, in urban areas, where smartphones rely on GNSS, MP and NLOS signals are the primary factors impeding accurate positioning. In this paper, with the goal of enhancing both the accuracy and robustness of smartphone GNSS positioning, we propose two methods. Firstly, an optimized particle filter method employing a Krill Herd Algorithm (KHA) is suggested for the integration of GNSS and Pedestrian Dead Reckoning (PDR). Secondly, a probabilistic approach is presented to identify faulty GNSS measurements using step distance information obtained from the PDR. Experimental tests were conducted using smartphones to evaluate the performance of the proposed method. The results demonstrate that both the KHA and fault detection methods effectively enhance the performance of integrated PDR and GNSS.

Automated Object Detection and Classification using Krill Herd Algorithm with Deep Learning on Surveillance Videos

Automated Object Detection and Classification using Krill Herd Algorithm with Deep Learning on Surveillance Videos

K-means Algorithm Based on Improved Krill Herd Algorithm and Calinski-Harabasz Index

K-means Algorithm Based on Improved Krill Herd Algorithm and Calinski-Harabasz Index

A Novel Long and Short-Term Memory Network-Based Krill Herd Algorithm for Explainable Art Sentiment Analysis in Interior Decoration Environment

Aiming at the problem that most existing models of art sentiment analysis only consider text encoding from the word level, this paper proposes a novel long and short-term memory network-based krill herd algorithm for explainable art sentiment analysis in interior decoration environment. Firstly, multi-scale convolution is used to capture local correlation of different granularity, so as to obtain more semantic information of different levels and form richer text representation. Then, a gating mechanism is introduced to control the path of sentiment information flowing to the aggregation layer. An improved krill swarm algorithm based on cosine control factor and Cauchy factor is proposed to solve the model. Finally, the full connection layer and argmax function are used to achieve sentiment classification. The experimental results show that compared with other advanced models, the proposed model can improve the accuracy of emotion classification by 2.3% and 0.8% respectively on two public data sets of IMDB and Yelp2014, and obtain the minimum root mean square error (RMSE).

Snow Cover Mapping Based on SNPP-VIIRS Day/Night Band: A Case Study in Xinjiang, China

Detailed snow cover maps are essential for estimating the earth’s energy balance and hydrological cycle. Mapping the snow cover across spatially extensive and topographically complex areas with less or no cloud obscuration is challenging, but the SNPP-VIIRS Day/Night Band (DNB) nighttime light data offers a potential solution. This paper aims to map snow cover distribution at 750 m resolution across the diverse 1,664,900 km2 of Xinjiang, China, based on SNPP-VIIRS DNB radiance. We implemented a swarm intelligent optimization technique Krill Herd algorithm, which finds the optimal threshold value by taking Otsu’s method as the objective function. We derived SNPP-VIIRS DNB snow maps of 14 consecutive scenes in December 2021, compared our snow-covered area estimations with those from MODIS and AMSR2 standard snow cover products, and generated composite snow maps by merging MODIS and SNPP-VIIRS DNB data. Results show that SNPP-VIIRS DNB snow maps are capable of providing reliable snow cover maps superior to MODIS and AMSR2, with an overall accuracy level of 84.66%. The composite snow maps at 500 m spatial resolution provided 55.85% more information on snow cover distribution than standard MODIS products and achieved an overall accuracy of 84.69%. Our study demonstrated the feasibility of snow cover detection in Xinjiang based on SNPP-VIIRS DNB, which can serve as a supplementary dataset for MODIS estimations where clouded pixels are present.

A Chaotic Krill Herd Technique for Solving Combined Economic Emission Dispatch

The economic power dispatch (EPD) problem aims to find the optimal operational settings of power generating units with minimizing the entire production costs of generation and all system constraints are kept into their acceptable limitations. Meanwhile, achieving this economical operation based on the reduction of the fuel cost and fossil fuel fired power plant emission should be as little as possible. Another nonlinear framework, with contradictory objectives, called combined economical emission power dispatch (CEEPD) is considered as the target framework in this study. The CEEPD framework is subjected to a set of operational equality and inequality constraints. In terms of the solution methodology, this paper is focused at developing a novel optimization algorithm called Chaotic Krill Herd Algorithm (CKHA) as an efficient tool for solving non-convex EPD and CEEPD frameworks. The proposed CKHA is tested on several IEEE test systems for refining its convergence and therefore aggregating the overall solution method. The numerical results clearly show the dominance of CKHA in finding the competitive best fitness functions compared with the advanced methods those published in the literature.

A comparative study of metaheuristics algorithms based on their performance of complex benchmark problems

Metaheuristic approaches with extremely important improvements are very promising in the solution of intractable optimization problems. The objective of the present study is to test the capability of applications and compare the performance of the four selected algorithms from “classical” (simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE)) and “new generation” (firefly algorithm (FFA), krill herd (KH), grey wolf optimization (GWO), and symbiotic organism search (SOS)) each by solving selected benchmark problems that are used in the literature for algorithm testing purpose. The selected test problems had very complex objective functions and associated constraints with multiple local optima. Among all selected algorithms, the “new generation” SOS and KH algorithm successfully solved most of all the selected benchmark problems and achieved the best solution for most of them. Among four “classical” algorithms, DE, and PSO effectively attained the optimal solution which was very close to the best one. However, the “new generation” algorithm performed much better than the “classical” one. Therefore, no firm conclusion can be done about the universally best algorithm and their performance may be varied for different benchmark problems. However, in this study for the seven selected test problems, SOS and KH exhibited the most promising result and great potential with respect to execution time also. This study gives some insights to use SOS and KH as the best-performing algorithms to the novice user who can easily get lost in the plethora of large optimization algorithms.

Network energy optimization and intelligent routing in WSN applicable for IoT using self‐adaptive coyote optimization algorithm

SummaryThe Internet of Things (IoT) is a recent wireless telecommunications platform, which contains a set of sensor nodes linked by wireless sensor networks (WSNs). These approaches split the sensor nodes into clusters, in which each cluster consists of an exclusive cluster head (CH) node. The major scope of this task is to introduce a novel CH selection in WSN applicable to IoT using the self‐adaptive meta‐heuristic algorithm. This paper aids in providing the optimal routing in the network based on direct node (DN) selection, CH selection, and clone cluster head (CCH) selection. DNs are located near the base station, and it is chosen to avoid the load of CH. The adoption of the novel self‐adaptive coyote optimization algorithm (SA‐COA) is used for the DN selection and CCH selection. When the nodes are assigned in the network, DN and CCH selection is performed by the proposed SA‐COA. Then, the computation of residual energy helps to select the CH, by correlating with the threshold energy. CCH is proposed to copy the data from the CH to avoid the loss of data in transmitting. By forming the CCH, the next CH can be easily elected with the optimal CCH using SA‐COA. From the simulation findings, the best value of the designed SA‐COA‐LEACH model is secured at 1.14%, 3.17%, 1.18%, and 7.33% progressed than self‐adaptive whale optimization algorithm (SAWOA), cyclic rider optimization algorithm (C‐ROA), krill herd algorithm (KHA), and COA while taking several nodes 50. The proposed routing of sensor networks specifies better performance than the existing methods.