PH-SHOWOA: Parallel hybrid SHO-WOA for VRPSPDTW.

This research implements the whale optimization algorithm (WOA) and spotted hyena optimizer (SHO) in inverse scattering to regenerate the conductor shape concealed in the half-space. TM waves are irradiated from the other half-space to a perfect conductor with an unknown shape buried in one half-space. The scattered field measured outside the conductor surface with the boundary condition is used to reconstruct the object using the WOA and SHO algorithms. Several scenarios of reconstruction accuracy were compared for the WOA and SHO. The numerical simulations prove that the WOA has a better reconstruction capability.

The Robust Vehicle Routing Problem with Time Windows has been gaining popularity over the past few years due to its focus on tackling uncertainty inherent to real world problems. Most of the current approaches in generating robust solutions require prior knowledge on the uncertainties, such as uncertainties in travel time. Hence, they are less than favorable to use in the absence of data, i.e., in the case of data starvation. In this paper, we present an evolutionary algorithm that in the absence of data on travel time uncertainty, provides a decision maker with a collection of solutions, each with a corresponding level of trade-off between total travel distance and solution robustness. In particular, we present a novel realization of route flexibility and its relation to solution robustness. Furthermore, we propose a bi-objective evolutionary algorithm for the vehicle routing problem with time windows where the objectives are (a) total travel distance and (b) solution flexibility. The proposed algorithm is tested on the well-known Solomon benchmarks and a trade-off analysis between total distance and solution flexibility is provided based on the obtained test results. Based on observations from the trade-off analysis, a number of suggestions to improve the current logistics system are provided.

The study aims to optimize the vehicle routing problem, considering infeasible routing, to minimize losses for the company. Firstly, a vehicle routing model with hard time windows and infeasible route constraints is established, considering both the minimization of total vehicle travel distance and the maximization of customer satisfaction. Subsequently, a Floyd-based improved genetic algorithm that incorporates local search is designed. Finally, the computational experiment demonstrates that compared with the classic genetic algorithm, the improved genetic algorithm reduced the average travel distance by 20.6% when focusing on travel distance and 18.4% when prioritizing customer satisfaction. In both scenarios, there was also a reduction of one in the average number of vehicles used. The proposed method effectively addresses the model introduced in this study, resulting in a reduction in total distance and an enhancement of customer satisfaction.

A comprehensive meta-analysis of emerging swarm intelligent computing techniques and their research trend

The Vehicle Routing Problem with Time Windows is a complete NP combinatorial problem in which product deliveries to customers must be made under certain time constraints. This problem can be solved from a single objective approach, well studied in the state of the art, in which the objective of the total travel distance or the size of the fleet (number of vehicles) is generally minimized. However, recent studies have used a multiobjective approach (Multiobjective Vehicle Routing Problem with Time Windows, MOVRPTW) that solves the problem from a viewpoint closer to reality. This work presents a new multiobjective memetic algorithm based on the GRASP (Greedy Randomized Adaptive Search Procedures) algorithm called MOMGRASP for the minimization of three objectives in MOVRPTW (total travel time, waiting time of customers to be attended, and balance of total travel time between routes). The results of the experimentation carried out with 56 problems proposed by Solomon and 45 problems proposed by Castro-Gutiérrez show that the proposed algorithm finds better solutions in these three objectives and competitive solutions than those reported by Zhou (compared to LSMOVRPTW algorithm and optimizing 5 objectives: number of vehicles, total travel distance, travel time of the longest route, total waiting time due to early arrivals, and total delay time due to late arrivals) and by Baños (versus the MMOEASA algorithm in two scenarios; case 1: total travel distance and balance of distance and case 2: total travel distance and balance of workload).

Spotted Hyena Optimizer (SHO) is a population-based metaheuristic algorithm inspired by the spotted hyenas’ social behavior, and it has been developed to solve global optimization problems. SHO has shown superior performance over its competitive metaheuristic algorithms in solving benchmark function optimization and engineering design problems. However, it suffers from getting stuck in local optima due to its lack of exploration while solving multi-modal optimization problems. This article proposes an improved SHO, quantum SHO (QSHO), inspired by quantum computing. The QSHO implements a quantum computing mechanism to promote its exploration ability. The novel method is tested on well-known IEEE CEC2013 and IEEE CEC2017 benchmark suits with 30 and 50 dimensions and four real-world engineering optimization problems. The results of QSHO are compared with that of Classical SHO, improved SHO (ISHO), Modified SHO (MSHO), Oppositional SHO with mutation operator (OBL-MO-SHO), SHO with space transformation search (STS-SHO), Quantum Salp Swarm Algorithm (QSSA), and Chimp Optimization Algorithm (ChOA). The results are analyzed using the Wilcoxon Signed Rank Test (WSRT) and Friedman Test. The empirical results show that QSHO statistically outperforms other compared algorithms for benchmark problem suits with 30 and 50 dimensions. According to Friedman Test statistics, the QSHO algorithm ranked first and second in solving CEC2013 30D and 50D, respectively, whereas it ranked first in both solving CEC2017 30D and 50D. In addition, we have assessed the QSHO in four real-world engineering optimization problems, and the QSHO statistically outperforms the competitive algorithms.

The aim of the present study was to firstly, quantify the external training load (TL) of semi-professional soccer players during an annual season and secondly, to examine the influence of one (1MW) and two (2MW) match weekly microcycles. Data were collected from 24 semi-professional outfield soccer players during the 2018-2019 annual season using micro-electromechanical system (MEMS) devices for the following variables: Training duration (min), total distance (TD), Player Load (PL), high speed running (HSR) distance (5.5-7.0 m/s), and acceleration (ACC) efforts (>2 m/s2). Training sessions were defined as days before match day (i.e. MD minus), with match weeks broken down as either 1MW or 2MW. Data revealed higher TD, PL, and HSR distance on MD and MD-5 when compared to all other MD codes. MD-4 displayed significantly higher values compared to MD-1 (mean differences (M diff): TD: 785 ± 158 m; PL: 29 ± 9 au; HSR: 192 ± 63 m; ACC: 15 ± 3 #) and MD-2 (M diff: TD: 279 ± 137 m; HSR: 127 ± 54 m). During 2MW scenarios, both TD (M diff: 685 ± 328 m) and PL (M diff: 33 ± 14 au) were higher on MD-1 when compared to 1MW. However, lower values were observed for duration and HSR on MD-2 and MD-4 during 2MW compared to 1MW scenarios.These data suggest that there appears to be a progressive reduction in TD, PL, HSR and ACC leading into competitive matches based on MD- analysis. However, some variability exists in TL prescription as a result of different MW scenarios (i.e. 1MW vs. 2MW).

Ensuring accurate estimation of evaporation is weighty for effective planning and judicious management of available water resources for agricultural practices. Thus, this work enhances the potential of support vector regression (SVR) optimized with a novel nature-inspired algorithm, namely, Slap Swarm Algorithm (SVR-SSA) against Whale Optimization Algorithm (SVR-WOA), Multi-Verse Optimizer (SVR-MVO), Spotted Hyena Optimizer (SVR-SHO), Particle Swarm Optimization (SVR-PSO), and Penman model (PM). Daily EP (pan-evaporation) was estimated in two different agro-climatic zones (ACZ) in northern India. The optimal combination of input parameters was extracted by applying the Gamma test (GT). The outcomes of the hybrid of SVR and PM models were equated with recorded daily EP observations based on goodness-of-fit measures along with graphical scrutiny. The results of the appraisal showed that the novel hybrid SVR-SSA-5 model performed superior (MAE = 0.697, 1.556, 0.858 mm/day; RMSE = 1.116, 2.114, 1.202 mm/day; IOS = 0.250, 0.350, 0.303; NSE = 0.0.861, 0.750, 0.834; PCC = 0.929, 0.868, 0.918; IOA = 0.960, 0.925, 0.956) than other models in testing phase at Hisar, Bathinda, and Ludhiana stations, respectively. In conclusion, the hybrid SVR-SSA model was identified as more suitable, robust, and reliable than the other models for daily EP estimation in two different ACZ.

Purpose The purpose of this study is to provide a novel portfolio asset prediction by means of the modified deep learning and hybrid meta-heuristic concept. In the past few years, portfolio optimization has appeared as a demanding and fascinating multi-objective problem, in the area of computational finance. Yet, it is accepting the growing attention of fund management companies, researchers and individual investors. The primary issues in portfolio selection are the choice of a subset of assets and its related optimal weights of every chosen asset. The composition of every asset is chosen in a manner such that the total profit or return of the portfolio is improved thereby reducing the risk at the same time. Design/methodology/approach This paper provides a novel portfolio asset prediction using the modified deep learning concept. For implementing this framework, a set of data involving the portfolio details of different companies for certain duration is selected. The proposed model involves two main phases. One is to predict the future state or profit of every company, and the other is to select the company which is giving maximum profit in the future. In the first phase, a deep learning model called recurrent neural network (RNN) is used for predicting the future condition of the entire companies taken in the data set and thus creates the data library. Once the forecasting of the data is done, the selection of companies for the portfolio is done using a hybrid optimization algorithm by integrating Jaya algorithm (JA) and spotted hyena optimization (SHO) termed as Jaya-based spotted hyena optimization (J-SHO). This optimization model tries to get the optimal solution including which company has to be selected, and optimized RNN helps to predict the future return while using those companies. The main objective model of the J-SHO-based RNN is to maximize the prediction accuracy and J-SHO-based portfolio asset selection is to maximize the profit. Extensive experiments on the benchmark datasets from real-world stock markets with diverse assets in various time periods shows that the developed model outperforms other state-of-the-art strategies proving its efficiency in portfolio optimization. Findings From the analysis, the profit analysis of proposed J-SHO for predicting after 7 days in next month was 46.15% better than particle swarm optimization (PSO), 18.75% better than grey wolf optimization (GWO), 35.71% better than whale optimization algorithm (WOA), 5.56% superior to JA and 35.71% superior to SHO. Therefore, it can be certified that the proposed J-SHO was effective in providing intelligent portfolio asset selection and prediction when compared with the conventional methods. Originality/value This paper presents a technique for providing a novel portfolio asset prediction using J-SHO algorithm. This is the first work uses J-SHO-based optimization for providing a novel portfolio asset prediction using the modified deep learning concept.

Data classification has been considered extensively in different fields, such as machine learning, artificial intelligence, pattern recognition, and data mining, and the expansion of classification has yielded immense achievements. The automatic classification of maintenance data has been investigated over the past few decades owing to its usefulness in construction and facility management. To utilize automated data classification in the maintenance field, a data classification model is implemented in this study based on the analysis of different mechanical maintenance data. The developed model involves four main steps: (a) data acquisition, (b) feature extraction, (c) feature selection, and (d) classification. During data acquisition, four types of dataset are collected from the benchmark Google datasets. The attributes of each dataset are further processed for classification. Principal component analysis and first-order and second-order statistical features are computed during the feature extraction process. To reduce the dimensions of the features for error-free classification, feature selection was performed. The hybridization of two algorithms, the Whale Optimization Algorithm (WOA) and Spotted Hyena Optimization (SHO), tends to produce a new algorithm—i.e., a Spotted Hyena-based Whale Optimization Algorithm (SH-WOA), which is adopted for performing feature selection. The selected features are subjected to a deep learning algorithm called Recurrent Neural Network (RNN). To enhance the efficiency of conventional RNNs, the number of hidden neurons in an RNN is optimized using the developed SH-WOA. Finally, the efficacy of the proposed model is verified utilizing the entire dataset. Experimental results show that the developed model can effectively solve uncertain data classification, which minimizes the execution time and enhances efficiency.

Optimal modification of heating, ventilation, and air conditioning system performances in residential buildings using the integration of metaheuristic optimization and neural computing

In the field of operations research, optimizing vehicle routing and scheduling plays a critical role in enhancing economic efficiency while reducing environmental impacts. In particular, the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) is a popular variant of the classical vehicle routing problem (VRP) that places emphasis on operational sustainability and efficiency. Despite its popularity, compared to its static counterpart, hardly any attention has been given to the dynamic variant even though many routing scenarios require re-routing midday as unexpected customer orders arrive. To close this gap, this paper addresses the Dynamic Vehicle Routing Problem with Simultaneous Pickup and Delivery (DVRPSPD), a recently proposed variant of the VRPSPD. A loading strategy is proposed which takes into account the unusual characteristics that arise from combining dynamic requests with simultaneous pickup and delivery requests. This loading strategy is applied in conjunction with a genetic algorithm (GA) which employs an alteration of the popular Best-Cost-Route-Crossover (BCRC). The proposed GA, referred to as GA-BCRCD, alongside the loading strategy, demonstrates significant enhancements in solution quality compared to the memetic algorithm previously applied to these instances. For some instances, the proposed approach finds solutions with more than a 25% reduction in total distance travelled by vehicles.

The sixth generation (6G) is envisioned to be a spawned key technology that will support the ubiquitous and seamless connection of a massive number of Internet-of-Things (IoT) devices. The extremely high data rate, low end-to-end delay, high mobility of IoT devices propel the desideratum of extenuating the concern of reducing the energy consumption, i.e., green communication. Hence, in this article, we address the concern of green communication in 6G-enabled massive IoT devices by following the cluster-based data dissemination in the network. We propose a novel hybrid whale spotted hyena optimization (HWSHO) algorithm by synthesizing the whale optimizer algorithm (WOA) with exploitation capabilities of spotted hyena optimizer (SHO). We perform a simulation experimental study that shows the supreme performance of our proposed technique over the most recent proposed energy-efficient data dissemination methods. The proposed technique is an exemplary solution that could be pertinent to various hostile applications seeking green communication of 6G-enabled IoT devices.

The bipartite traveling tournament problem (BTTP) is an NP-complete scheduling problem whose solution is a double round-robin inter-league tournament with minimum total travel distance. The 2n-team BTTP is a variant of the well-known traveling salesman problem (TSP), albeit much harder as it involves the simultaneous coordination of 2n teams playing a sequence of home and away games under fixed constraints, rather than a single entity passing through the locations corresponding to the teams' home venues. As the BTTP requires a distance-optimal schedule linking venues in close proximity, we provide an approximation algorithm for the BTTP based on an approximate solution to the corresponding TSP. We prove that our polynomial-time algorithm generates a 2n-team inter-league tournament schedule whose total distance is at most 1 + 2c/3 + (3 − c)/(3n) times the total distance of the optimal BTTP solution, where c is the approximation factor of the TSP. In practice, the actual approximation factor is far better; we provide a specific example by generating a nearly-optimal inter-league tournament for the 30-team National Basketball Association, with total travel distance just 1.06 times the trivial theoretical lower bound.

This study focuses on determining an optimal distribution route for organic porridge products produced by a company and delivered to multiple outlets. Each outlet is visited exactly once, and the delivery process starts and ends at the same outlet. A total of 44 outlets are considered, which are initially divided into nine distribution routes. To improve distribution efficiency, this study proposes reorganizing the outlets into only three distribution routes. Each route formulation is modeled as a Traveling Salesman Problem (TSP). The optimization of the three TSP cases is carried out using a Genetic Algorithm (GA). In the GA implementation, the order of outlets along a route is encoded as a chromosome consisting of a sequence of genes. The fitness function is defined based on the total travel distance, where a smaller value indicates a better solution. The results show that increasing the number of iterations and the size of population, which is the number of candidate routes considered at each step, can reduce the total travel distance up to a certain point. The exact routes and their sequence of outlets can be visualized in a map depicting each of the three optimized paths. Keywords: Genetic Algorithm, distribution routing, total distance, Traveling Salesman Problem