Search

Planning problems are particularly important for the production processes of precast reinforced concrete elements. Currently used modeling of these processes is based on the flow shop problem. Flow shop models are usually used in Enterprise Resource Planning systems, which, however, may not take into account the specifics of the production of such elements. The article presents a new model for scheduling the production of reinforced concrete prefabricated elements, which is distinguished by the possibility of carrying out activities by more than one working group. An additional new constraint is the possibility of parallel performance of some works, which may occur during their production. Also, there will be an individual order of elements assumed for each of the activities. New objective functions will be considered - the sum of idle times of working groups and the total type changes of precast components. The presented scheduling model contains an NP-hard discrete optimization problem. For this reason, metaheuristics were used in the article to solve optimization problems: the simulated annealing algorithm and the tabu search algorithm. Verification of the results obtained with the use of these algorithms confirmed their high efficiency. The application of the presented scheduling model illustrates a practical case study showing the effectiveness of the used algorithms. [ABSTRACT FROM AUTHOR]

The Kolkata Paise Restaurant Problem is a challenging game in which n agents decide where to have lunch during their break. The game is not trivial because there are exactly n restaurants, and each restaurant can accommodate only one agent. We study this problem from a new angle and propose a novel strategy that results in greater utilization. Adopting a spatially distributed approach where the restaurants are uniformly distributed in the entire city area makes it possible for every agent to visit multiple restaurants. For each agent, the situation resembles that of the iconic traveling salesman, who must compute an optimal route through n cities. We rigorously prove probabilistic formulas that confirm the advantages of this policy and the increase in utilization. The derived equations generalize formulas that were previously known in the literature, which can be seen as special cases of our results. [ABSTRACT FROM AUTHOR]

This paper presents a novel dynamic Jellyfish Search Algorithm using a Simulated Annealing and disruption operator, called DJSD. The developed DJSD method incorporates the Simulated Annealing operators into the conventional Jellyfish Search Algorithm in the exploration stage, in a competitive manner, to enhance its ability to discover more feasible regions. This combination is performed dynamically using a fluctuating parameter that represents the characteristics of a hammer. The disruption operator is employed in the exploitation stage to boost the diversity of the candidate solutions throughout the optimization operation and avert the local optima problem. A comprehensive set of experiments is conducted using thirty classical benchmark functions to validate the effectiveness of the proposed DJSD method. The results are compared with advanced well-known metaheuristic approaches. The findings illustrated that the developed DJSD method achieved promising results, discovered new search regions, and found new best solutions. In addition, to further validate the performance of DJSD in solving real-world applications, experiments were conducted to tackle the task scheduling problem in cloud computing applications. The real-world application results demonstrated that DJSD is highly competent in dealing with challenging real applications. Moreover, it achieved gained high performances compared to other competitors according to several standard evaluation measures, including fitness function, makespan, and energy consumption. [ABSTRACT FROM AUTHOR]

The weapon target assignment (WTA) problem is an important task to tactical arrangements in military commitment operations. It describes the optimal method to allocate defense in opposition to threats in fighting situations. It is an NP-complete issue in which no accurate outcome for all conceivable situations is known. The time performance of created algorithms is a major challenge in modeling the WTA problem, which has only been lately considered in related papers. This article improves the recently developed algorithm called improved Tunicate Swarm Algorithm (iTSA) which is inspired by the natural behavior of tunicates to solve the WTA problem. The suggested method is compared with well-known metaheuristic approaches. The experimental findings show that the method presented works better than previous competing metaheuristic approaches. [ABSTRACT FROM AUTHOR]

In this article, we are presenting a new nature-inspired metaheuristic optimization algorithm that we call the Mycorrhiza Tree Optimization Algorithm (MTOA) focused on solving optimization problems. The algorithm is inspired by the relationship between trees and the Mycorrhiza Network (MN). In this relationship, there are interactions such as defense, communication, resource exchange and habitat colonization between the Trees and the MN. The Lotka–Volterra continuous systems (Predator–Prey, Cooperative and Competitive Models) were used for the design of the algorithm. To verify the efficiency of the algorithm, experiments with 36 mathematical functions were performed, but we are only presenting results of the functions with which comparisons were made with other methods. Hypothesis tests were also carried out, where in most of the results the MTOA algorithm was better by 70% and only in the experiments in 100 dimensions it was better by 60%. We believe that the algorithm has the potential to solve problems in control and optimization of neural networks, for which experimentation will be done as future work. [ABSTRACT FROM AUTHOR]

The aim of this manuscript is to introduce solutions to optimize economic dispatch of loads and combined emissions (CEED) in thermal generators. We use metaheuristics, such as particle swarm optimization (PSO), ant lion optimization (ALO), dragonfly algorithm (DA), and differential evolution (DE), which are normally used for comparative simulations, and evaluation of CEED optimization, generated in MATLAB. For this study, we used a hybrid model composed of six (06) thermal units and thirteen (13) photovoltaic solar plants (PSP), considering emissions of contaminants into the air and the reduction in the total cost of combustibles. The implementation of a new method that identifies and turns off the least efficient thermal generators allows metaheuristic techniques to determine the value of the optimal power of the other generators, thereby reducing the level of pollutants in the atmosphere. The results are presented in comparative charts of the methods, where the power, emissions, and costs of the thermal plants are analyzed. Finally, the comparative results of the methods were analyzed to characterize the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) and coordinated multi-point (CoMP) are two fundamental techniques considered for the fifth generation (5 G) of wireless communications. In this paper, a hybrid satellite-unmanned aerial vehicle (UAV) relay network (HSURN) is proposed where the UAV relays (URs) employ CoMP transmission to serve the terrestrial users (UEs). Furthermore, all UEs associated with the CoMP-URs form a single NOMA cluster. For this model, an optimization problem is formulated subject to the minimum quality of services (QoSs) requirements of the UEs, transmission power budgets and, successive interference cancellation (SIC), to select URs and allocate their transmission powers for the energy efficiency (EE) maximization. With this insight, first, a computationally efficient sub-optimal UR selection scheme is proposed. Then, the powers are allocated to the selected URs via the Lagrange multipliers optimization (LMO) method. Due to the non-convex nature of the considered problem, it is relatively difficult to be solved. Hence, a metaheuristic teaching-learning-based optimization (TLBO) algorithm is employed to achieve an efficient solution. Simulation results are provided to verify the effectiveness of the proposed sub-optimal relay selection scheme and the TLBO-based power allocation method compared to the LMO conventional method. Besides, the obtained results also reveal that the CoMP-NOMA transmission in the proposed scenario significantly improves the spectral efficiency (SE) and outage probability (OP) of the system compared to non-comp NOMA transmission case. [ABSTRACT FROM AUTHOR]

High spectral dimensionality of hyperspectral image (HSI) has brought great redundancy for data processing. Band selection (BS), as one of the most commonly used dimension reduction (DR) techniques, attempts to remove the redundant spectral bands, while maintaining good classification or detection rate for later applications. Gray wolf optimizer (GWO) algorithm is a meta-heuristic algorithm, and it is used for HSI BS. However, the convergence factor of the basic GWO is linearly decreased, leading to a slower convergence speed and increasing the probability of falling into local optimality. This article proposes a new hybrid gray wolf optimizer (HGWO) algorithm for HSI BS, which uses adaptive decreasing convergence factor instead of linear convergence factor to improve GWO convergence rate and combines category separability for initialization to avoid local optimality. Five nonlinear functions are used to test the convergence of the proposed HGWO algorithm, compared with the state-of-the-art optimization algorithms. Finally, the experimentations are performed on three widely used real hyperspectral datasets for HSI classification, and the experimental results show that band subsets selected by the proposed HGWO algorithm can obtain better classification accuracy compared with other global optimization algorithms. [ABSTRACT FROM AUTHOR]

Accurate classification of high-dimensional biomedical data highly depends on the efficient recognition of the data's main features which can be used to assist diagnose related diseases. However, due to the existence of a large number of irrelevant or redundant features in biomedical data, classification approaches struggle to correctly identify patterns in data without a feature selection algorithm. Feature selection approaches seek to eliminate irrelevant and redundant features to maintain or enhance classification accuracy. In this paper, a new wrapper feature selection method is proposed based on the chimp optimization algorithm (ChOA) for biomedical data classification. The ChOA is a newly proposed metaheuristic algorithm whose capability for solving feature selection problems has not been investigated yet. Two binary variants of the ChoA are introduced for the feature selection problem. In the first approach, two transfer functions (S-shaped and V-shaped) are used to convert the continuous version of ChoA to binary. In addition to the transfer function, the crossover operator is utilized in the second approach to improve the ChOA's exploratory behavior. To validate the efficiency of the proposed approaches, five publicly available high-dimensional biomedical datasets, and a few datasets from different domains such as life, text, and image are employed. The proposed approaches were then compared with six well-known wrapper-based feature selection methods, including multi-objective genetic algorithm (GA), particle swarm optimization (PSO), Bat algorithm (BA), ant colony optimization (ACO), firefly algorithm (FA), and flower pollination (FP) algorithm, as well as two standard filter-based feature selection methods using three different classifiers. The experimental results demonstrate that the proposed approaches can effectively remove the least significant features and improve classification accuracy. The suggested wrapper feature selection techniques also outperform the GA, PSO, BA, ACO, FA, FP, and other existing methods in the terms of the number of selected genes, and classification accuracy in most cases. [ABSTRACT FROM AUTHOR]

Spiking neural networks are the third generation of artificial neural networks that are inspired by a new brain-inspired computational model of ANN. Spiking neural network encodes and processes neural information through precisely timed spike trains. eSNN is an enhanced version of SNN, motivated by the principles of Evolving Connectionist System (ECoS), which is relatively a new classifier in the neural information processing area. The performance of eSNN is highly influenced by the values of its significant hyperparameters' modulation factor (mod), threshold factor (c), and similarity factor (sim). In contrast to the manual tuning of hyperparameters, automated tuning is more reliable. Therefore, this research presents an optimizer-based eSNN architecture, intended to solve the issue regarding optimum hyperparameters' values' selection of eSNN. The proposed model is named eSNN-SSA where SSA stands for salp swarm algorithm, which is a metaheuristic optimization technique integrated with eSNN architecture. For the integration of eSNN-SSA, Thorpe's standard model of eSNN is used with population rate encoding. To examine the performance of eSNN-SSA, various benchmarking data sets from the UCR/UAE time-series classification repository are utilized. From the experimental results, it is concluded that the salp swarm algorithm plays an effective role in improving the flexibility of the eSNN. The proposed eSNN-SSA offers solutions to conquer the disadvantages of eSNN in determining the best number of pre-synaptic neurons for time-series classification problems. The performance accuracy obtained by eSNN-SSA was on datasets spoken Arabic digits, articulatory word recognition, character trajectories, wafer, and GunPoint, i.e., 0.96, 0.97, 0.94, 1.0, and 0.94, respectively. The proposed approach outperformed standard eSNN in terms of time complexity. [ABSTRACT FROM AUTHOR]

Inspecting large-scale physical infrastructure assets such as powerlines from the air involves capturing data of thousands of features of an asset and its nearby surroundings, which makes inspection flight planning challenging. The objective of this article is to address the problems of automatically generating and optimizing flight paths and mission plans for airborne inspection of large linear infrastructure assets. This article proposes an automated feature-driven flight planning method using metaheuristic optimization with consideration of aircraft dynamic, sensor, terrain, and airfield location constraints. Results for flight planning the inspection of one of the world’s largest above-ground powerline networks are shown. Automated planning can reduce the human planning workload and enable the cost-saving benefits of automated aircraft to be realized through efficient deployment. Note to Practitioners—This work extends previous research and development of an automated aircraft guidance system for infrastructure inspection, by addressing the problem of automated flight planning. Often, deploying manned or automated aircraft reliably and efficiently for large-scale inspection is a challenge. Designing efficient flight paths and missions by hand is complex and time-consuming. Traditional “top-down” planning approaches use region or waypoint-based planning, neglecting the spatial layout of the assets to be inspected, possibly leading to more missions than required, inefficient flight paths, or missed data capture. Since inspecting a large asset with few aircraft can take many days, a solution was sought that can plan across multiple time scales (from second to second path planning to daily mission planning). A solution was designed that can build flight plans automatically from the “bottom-up” while optimizing performance objectives and satisfying constraints. Flight plans can either be used to aid pilots conducting an aerial inspection, as input to an automated line-tracking system for aircraft or UAVs or for estimating the cost of large-scale inspection operations. The presented method and results are motivated by the specific problem of large-scale powerline inspection and the approach may be applied to the inspection of other above-ground infrastructures, such as roads and railways. [ABSTRACT FROM AUTHOR]

Reserve evaluation is a very difficult and complex process. The most important and yet most challenging part of this process is grade estimation. Its difficulty derived from challenges in obtaining required data from the deposit by drilling boreholes, which is a very time-consuming and costly act itself. Classic methods which are used to model the deposit are based on some preliminary assumptions about reserve continuity and grade spatial distribution which are not true about all kind of reserves. In this paper, a multilayer perceptron (MLP) artificial neural network (ANN) is applied to solve the problem of ore grade estimation of highly sparse data from Zarshouran gold deposits in Iran. The network is trained using four metaheuristic algorithms in separate stages for each algorithm. These algorithms are artificial bee colony (ABC), genetic algorithm (GA), imperialist competitive algorithm (ICA), and particle swarm optimization (PSO). The accuracy of predictions obtained from each algorithm in each stage of experiments was compared with real gold grade values. We used unskillful value to check the accuracy and stability of each network. Results showed that the network trained with the ABC algorithm outperforms other networks that trained with other algorithms in all stages having the least unskillful value of 13.91 for validation data. Therefore, it can be more suitable for solving the problem of predicting ore grade values using highly sparse data. [ABSTRACT FROM AUTHOR]

The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experiments on a dual-arm robot, named as duAro. In terms of energy efficiency, the results show that dual-arm motions can save more energy than single-arm motions for an industrial robot. Furthermore, combining the robot configuration problem with metaheuristic approaches saves energy consumption and robot execution time when compared to motion planning with PID controllers alone. [ABSTRACT FROM AUTHOR]

This paper presents an approach to overcome economic load dispatch (ELD) using a metaheuristic algorithm. Economic load dispatch (ELD) is one of the most important problems in a power system, and solving it quickly is extremely important. The main problem that will be addressed in this paper is how to optimize the economy of the power grid with various operational limitations, the loss in transmission line power, and consider minimizing the fuel costs produced. In this study, some of the newest metaheuristics inspired by nature will be explored, namely Seagull Optimization Algorithm (SOA), Marine Predator Algorithm (MPA), Sine Tree-Seed Algorithm (STSA), Chimp Optimization Algorithm (ChOA), Equilibrium Optimizer (EO), and Giza Pyramids Construction (GPC). The performance appraisal of the method applied in this study was tested using 2 case studies, namely a system with 3 and 6 power system units. The results are presented by comparing between metaheuristic and mathematical methods. The experimental results is showed that the Sine Tree-Seed Algorithm (STSA) is presented the best performance with various case studies with constraints. [ABSTRACT FROM AUTHOR]

In this study, Modified Monarch Butterfly Optimization Algorithm (M2BO) is proposed by modeling stochastic processes in Monarch Butterfly Optimization (MBO) algorithm with different random distribution functions. The proposed M2BO algorithm has been firstly tested with benchmark functions and the results have been compared with the literature and classical MBO algorithm. In order to analyze the performance of the proposed M2BO algorithm for the real engineering problem, the feedback gain matrix (K matrix) for the control of the 3 Degree of Freedom (3 DOF) Hover system has been optimized. The results have been compared with classical MBO, DSO (Discrete Stochastic Optimization), and SMDO (Stochastic Multi-parameter Divergence Optimization) optimization algorithms. The obtained results have been compared on 3 DOF Hover simulation models and real-time 3 DOF Hover experimental sets. The performance of the proposed M2BO algorithm in benchmark and real engineering problem tests has been shown theoretically and experimentally. Thus, it has been shown that the performance of algorithms can be increased without changing the basic philosophy of algorithms by modeling stochastic processes in algorithms with random distributions other than a uniform distribution. In addition to these, it has been determined that distribution function-based contributions can be applied to many of these algorithms. [ABSTRACT FROM AUTHOR]

One of the most promising technologies to be employed in beyond 5G networks is based on the concept of cell-free (CF) massive MIMO, in which a predetermined set of access points (APs) jointly cooperate in the data transmission and reception to/from the user equipment (UE). In order to efficiently manage radio resources, the CF central processing unit implement uplink power control policies. These policies aim to optimize a given network utility function. In this paper, we investigate the max-min fairness optimization problem, in which the spectral efficiency performance of the UE with the worst channel conditions is prioritized, taking into account a per-UE power constraint and assuming linear maximum ratio combining at APs. Existing solutions have typically relied on second-order cone programming with convex approximations, which exhibit high computational complexity and scalability issues. Therefore, meta-heuristics (MHs) are explored as alternative optimization schemes, capable of providing (near)-optimal solutions with reasonable computational effort. Three MH approaches with different operation principles are compared. Numerical results show that the differential evolution algorithm exhibits the best trade-off between solution quality and run time, being able to reach (near)-optimal solutions faster than the bisection approach while coping with the scalability issues of the geometric programming-based algorithm. [ABSTRACT FROM AUTHOR]

Determining the topology, layout, and size of structural elements is one of the most important aspects in designing steel seismic-resistant structures. Optimization of these parameters is beneficial to find the lightest weight of the structure, thus reducing construction cost. This study compares the performance of three metaheuristic algorithms, namely, Particle Swarm Optimization (PSO), Symbiotic Organisms Search (SOS), and Differential Evolution (DE). Three study cases are used in order to find the lightest structural weight without violating constraints based on SNI 1726:2019, SNI 1729:2020, and SNI 7860:2020. The results of this study show that SOS has better performance than other algorithms. [ABSTRACT FROM AUTHOR]

The metaheuristic optimization algorithms are relatively new optimization algorithms introduced to solve optimization problems in recent years. For example, the firefly algorithm (FA) is one of the metaheuristic algorithms inspired by the fireflies' flashing behavior. However, its weakness in terms of exploration and early convergence has been pointed out. In this paper, two approaches were proposed to improve the FA. In the first proposed approach, a new improved opposition-based learning FA (IOFA) method was presented to accelerate the convergence and improve the FA's exploration capability. In the second proposed approach, a symbiotic organisms search (SOS) algorithm improved the exploration and exploitation of the first approach; two new parameters set these two goals, and the second approach was named IOFASOS. The purpose of the second method is that in the process of the SOS algorithm, the whole population is effective in the IOFA method to find solutions in the early stages of implementation, and with each iteration, fewer solutions are affected in the population. The experiments on 24 standard benchmark functions were conducted, and the first proposed approach showed a better performance in the small and medium dimensions and exhibited a relatively moderate performance in the higher dimensions. In contrast, the second proposed approach was better in increasing dimensions. In general, the empirical results showed that the two new approaches outperform other algorithms in most mathematical benchmarking functions. Thus, The IOFASOS model has more efficient solutions. [ABSTRACT FROM AUTHOR]

The firefly algorithm and cuckoo search are the meta-heuristic algorithms efficient to determine the solution for the searching and optimization problems. The current work proposes an integrated concept of quantum-inspired firefly algorithm with cuckoo search (IQFACS) that adapts both algorithms' expedient attributes to optimize the solution set. In the IQFACS algorithm, the quantum-inspired firefly algorithm (QFA) ensures the diversification of fireflies-based generated solution set using the superstitions quantum states of the quantum computing concept. The cuckoo search (CS) algorithm uses the Lévy flight attribute to escape the QFA from the premature convergence and stagnation stage more effectively than the quantum principles. Here, the proposed algorithm is applied for the application of optimal path planning. Before using the proposed algorithm for path planning, the algorithm is tested on different optimization benchmark functions to determine the efficacy of the proposed IQFACS algorithm than the firefly algorithm (FA), CS, and hybrid FA and CS algorithm. Using the proposed IQFACS algorithm, path planning is performed on the satellite images with vegetation as the focused region. These satellite images are captured from Google Earth and belong to the different areas of India. Here, satellite images are converted into morphologically processed binary images and considered as maps for path planning. The path planning process is also executed with the FA, CS, and QFA algorithms. The performance of the proposed algorithm and other algorithms are accessed with the evaluation of simulation time and the number of cycles to attain the shortest path from defined source to destination. The error rate measure is also incorporated to analyze the overall performance of the proposed IQFACS algorithm over the other algorithms. [ABSTRACT FROM AUTHOR]

خؽارزمية عذيخة الدخقاط )MCA )ىي خؽارزمية )metaheuristic )قائمة عمى الطبيعة والتي تحاكي عمى خخيطة ً ا عمى خؽارزمية عذيخة ميخكات بناء ً الدمؽك الحكي لحيؽان الدخقاط. تقجم ىحه الؽرقة تحدين فؽضؽية واستخاتيجية التقاطع )CC-MCA .)تعمل ىاتان االستخاتيجيتان عمى زيادة تنؽيع وتكثيف الخؽارزمية المقتخحة وتعديد القجرة عمى البحث إليجاد المديج مؼ الحمؽل عالية الجؽدة. تػ حل مذكمة حقيبة العيخ 0-1 بؽاسطة MCA األساسي والندخة المحدنة مؼ ىحه نة ّ الخؽارزمية المحد CC-MCA .تػ اختبار أداء ىحه الخؽارزميات عمى مدائل ذات أبعاد منخفضة وعالية. أظيخت النتائج التجخيبية أن الخؽارزمية المقتخحة تغمبت عمى الخؽارزمية األساسية مؼ حيث جؽدة الحل وسخعتو واكتدبت األمثمية مع مذاكل األبعاد المنخفضة. عالوة عمى ذلغ ، في مذاكل األبعاد العالية ، ليا نتائج تنافدية مقارنة بالخؽارزميات األخخى. [ABSTRACT FROM AUTHOR]

Researchers from different domains have developed several metaheuristic algorithms that are inspired by the biological phenomenon. In this work, we propose a novel algorithm that is inspired by the prey–predator interaction of animals. In the proposed algorithm, two random populations are considered as a predator and preys. The energy gain for both predators and preys calculated based on their body mass and the interaction between predators and their mutual preys. The best predator (i.e., predator with the highest energy gain) performs a local search (exploitation). The other solution members in prey population facilitate the exploration of the search space. The proposed algorithm is evaluated by three phases. Firstly, a set of 16 mathematical functions are considered to test different characteristics of predator–prey optimization (PPO) algorithm as a optimizer. Secondly, it is evaluated by seven datasets as a solver for feature selection problem. The proposed algorithm as an global optimizer has been compared with other metaheuristic algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), ant lion optimizer (ALO), gray wolf optimizer (GWO), whale optimization algorithm (WOA), differential evolution (DE), sine–cosine algorithm (SCA), moth flame optimization (MFO), and salp swarm algorithm (SSA). The results indicate that PPO improves convergence rate by 33.8%, 24.2%, 37.3%, 10.5%, 12.4%, 22.7%, 18.6%, 30.7%, and 29.1% in average compared to GA, PSO, ALO, GWO, WOA, DE, SCA, MFO, and SSA, respectively. Furthermore, PPO obtains a higher Performance Index around 5–10% than competitor approaches with respect to exploration–exploitation balance and a high degree of stability. Secondly, the performance of PPO for feature selection issues demonstrates the applicability PPO algorithm in solving real problems with unknown search spaces. [ABSTRACT FROM AUTHOR]

Tool traveling and tool switching time constitute almost seventy percent (70%) of the total time consumed in drilling process. This fact that 70% of the total time is nonproductive and does not add any value to the job, grabs attention of the researchers and the industrialist for optimization. A literature on drilling process revealed that very few studies have been done on hybridization of metaheuristics for optimization of tool travel time. This research gap is the motivation of the present study. In this study, two metaheuristic approaches -- the shuffled frog leaping algorithm (SFLA) and ant colony optimization (ACO) were hybridized. With respect to hybridization of SFLA and ACO, this study signifies its originality and novelty in which main objective is to minimize the tool travel time. The literature review also revealed that the shortest path generated through commercially available software is not optimal all the time. This aspect emphasizes the application of metaheuristic algorithms on the real-world industrial problems. In this study, the proposed hybrid algorithm was applied to drilling of omega plate which is used in automobile manufacturing industry. The results of the proposed hybrid algorithm were compared with those of manual drilling path and software generated path. The results obtained through proposed hybrid algorithm were improved by 11.1% when compared to results of manual drilling path. The results of proposed algorithm were also better than results of commercial software Creo 6.0 and Siemens NX by 5.9% each. This showed that hybrid algorithm outperformed the commercially available software. This not only validates the efficacy of proposed hybrid algorithm, but also indicates the significance of the metaheuristic algorithm applications in industrial optimization problems. [ABSTRACT FROM AUTHOR]

Improving the efficiency and sustainability of distribution networks (DNs) is nowadays a challenging objective both for large networks and microgrids connected to the main grid. In this context, a crucial role is played by the so-called network reconfiguration problem, which aims at determining the optimal DN topology. This process is enabled by properly changing the close/open status of all available branch switches to form an admissible graph connecting network buses. The reconfiguration problem is typically modeled as an NP-hard combinatorial problem with a complex search space due to current and voltage constraints. Even though several metaheuristic algorithms have been used to obtain—without guarantees—the global optimal solution, searching for near-optimal solutions in reasonable time is still a research challenge for the DN reconfiguration problem. Facing this issue, this article proposes a novel effective optimization framework for the reconfiguration problem of modern DNs. The objective of reconfiguration is minimizing the overall power losses while ensuring an enhanced DN voltage profile. A multiple-step resolution procedure is then presented, where the recent Harris hawks optimization (HHO) algorithm constitutes the core part. This optimizer is here intelligently accompanied by appropriate preprocessing (i.e., search space preparation and initial feasible population generation) and postprocessing (i.e., solution refinement) phases aimed at improving the search for near-optimal configurations. The effectiveness of the method is validated through numerical experiments on the IEEE 33-bus, the IEEE 85-bus systems, and an artificial 295-bus system under distributed generation and load variation. Finally, the performance of the proposed HHO-based approach is compared with two related metaheuristic techniques, namely the particle swarm optimization algorithm and the Cuckoo search algorithm. The results show that HHO outperforms the other two optimizers in terms of minimized power losses, enhanced voltage profile, and running time. Note to Practitioners—This article is motivated by the emerging need for effective network reconfiguration approaches in modern power distribution systems, including microgrids. The proposed metaheuristic optimization strategy allows the decision maker (i.e., the distribution system operator) to determine in reasonable time the optimal network topology, minimizing the overall power losses and considering the system operational requirements. The proposed optimization framework is generic and flexible, as it can be applied to different architectures both of large distribution networks (DNs) and microgrids, considering various types of system objectives and technical constraints. The presented strategy can be implemented in any decision support system or engineering software for power grids, providing decision makers with an effective information and communication technology tool for the optimal planning of the energy efficiency and environmental sustainability of DNs. [ABSTRACT FROM AUTHOR]

The task assignment problem (TAP) is concerned with assigning a set of tasks to a set of agents subject to the limited processing and memory capacities of each agent. The objective to be minimized is the total assignment cost and total communication cost. TAP is a relevant model for many practical applications, yet solving the problem is computationally challenging. Most of the current metaheuristic algorithms for TAP adopt population-based search frameworks, whose search behaviors are usually difficult to analyze and understand due to their complex features. In this work, unlike previous population-based solution methods, we concentrate on a single trajectory stochastic local search model to solve TAP. Especially, we consider TAP from the perspective of a grouping problem and introduce the first probability learning-based local search algorithm for the problem. The proposed algorithm relies on a dual probability learning procedure to discover promising search regions and a gain-based neighborhood search procedure to intensively exploit a given search region. We perform extensive computational experiments on a set of 180 benchmark instances with the proposed algorithm and the general mixed integer programming solver CPLEX. We assess the composing ingredients of the proposed algorithm to shed light on their impacts on the performance of the algorithm. Note to Practitioners—This work is motivated by the problem of program modules designing and task allocation in parallel and distribution systems. It can also be applied to deal with job (task) grouping problems in practical industrial applications. This article presents a novel and effective learning-based local search algorithm to obtain high-quality solutions for the considered problem. The results of numerical experiments and comparisons show that our algorithm can achieve good search performances on problem instances of different scales and difficulties. Afterward, we use the proposed solution method to solve a real-life open-order slab assignment problem, which is derived from the production planning of silicon steel for an iron and steel company. The learning techniques of the proposed algorithm are of general interest and can be used in search algorithms for solving other real-life optimization problems with grouping features. For future research, we will design solution methods based on these learning techniques to address other practical optimization problems. [ABSTRACT FROM AUTHOR]

In this article, a new metaheuristic algorithm named average differential evolution with local search (ADE-LS) has been developed and implemented to find the optimal coefficients of unknown infinite impulse response (IIR) system as a system identifier. The developed method minimizes the error between unknown system output and the adaptive IIR filter output. Rapid convergence is aimed for the global solution in system identification problem using the ADE-LS based adaptive IIR filter modelling with local search. In this way, more precise prediction of filter coefficients is ensured in the filter design with multimodal error surface. ADE-LS algorithm is applied to four benchmarked IIR systems commonly studies in literature to show its performance. Results found by using ADE-LS are compared to other methods reported in terms of convergence rate and the mean square error value. The attained results approve the efficiency of the suggested method. [ABSTRACT FROM AUTHOR]

This paper presents a novel meta-heuristic algorithm called Ali Baba and the forty thieves (AFT) for solving global optimization problems. Recall the famous tale of Ali Baba and the forty thieves, where Ali Baba once saw a gang of forty thieves enter a strange cave filled with all kinds of treasures. The strategies pursued by the forty thieves in the search for Ali Baba inspired us to design ideas and underlie the basic concepts to put forward the mathematical models and implement the exploration and exploitation processes of the proposed algorithm. The performance of the AFT algorithm was assessed on a set of basic benchmark test functions and two more challenging benchmarks called IEEE CEC-2017 and IEEE CEC-C06 2019 benchmark test functions. These benchmarks cover simple and complex test functions with various dimensions and levels of complexity. An extensive comparative study was performed between the AFT algorithm and other well-studied algorithms, and the significance of the results was proved by statistical test methods. To study the potential performance of AFT, its further development is discussed and carried out from five aspects. Finally, the applicability of the AFT algorithm was subsequently demonstrated in solving five engineering design problems. The results in both benchmark functions and engineering problems show that the AFT algorithm has stronger performance than other competitors' algorithms. [ABSTRACT FROM AUTHOR]

In the modern world, it is necessary to monitor a user's mobility in everyday life to provide advanced mobile services. Mostly, the location-based services (LBS) depend on both the present and future location of the user, which shows the increasing demand on predicting the user's future location. The prediction of trajectories among two different locations is very important as it aids to optimize travel paths among the locations. Moreover, existing techniques rely on high-quality data to deliver optimal results. In this research, we proposed a new optimized deep learning architecture for mobility tracing. The prediction model is based on Optimized DCNN (Deep Conventional Neural Network) that is already trained with the traced location of the user and from the user's trained movement the model can predict its next location. For precise prediction, the weight and activation function of DCNN is optimally selected with the aid of HS-EH (Hybrid SeaLion -Elephant Herding) algorithm, which is a hybridization of Sea lion Optimization (SLnO) and Elephant Herding Optimization (EHO). Eventually, the proposed method performance is executed in MATLAB 2019a and compared with state-of-art methods with certain performance metrics. Especially, the accuracy of the proposed DCNN + HS-EH algorithm at training rate 10 is 82.96%, 48.88%, 79.27%, and 61.67% better than existing methods. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, the authors aim to examine and comparatively evaluate a recently-developed metaheuristic called crystal structure algorithm (CryStAl) – which is inspired by the symmetries in the internal structure of crystalline solids – in solving engineering mechanics and design problems. Design/methodology/approach: A total number of 20 benchmark mathematical functions are employed as test functions to evaluate the overall performance of the proposed method in handling various functions. Moreover, different classical and modern metaheuristic algorithms are selected from the optimization literature for a comparative evaluation of the performance of the proposed approach. Furthermore, five well-known mechanical design examples are utilized to examine the capability of the proposed method in dealing with challenging optimization problems. Findings: The results of this study indicated that, in most cases, CryStAl produced more accurate outputs when compared to the other metaheuristics examined as competitors. Research limitations/implications: This paper can provide motivation and justification for the application of CryStAl to solve more complex problems in engineering design and mechanics, as well as in other branches of engineering. Originality/value: CryStAl is one of the newest metaheuristic algorithms, the mathematical details of which were recently introduced and published. This is the first time that this algorithm is applied to solving engineering mechanics and design problems. [ABSTRACT FROM AUTHOR]

This work proposes a methodology to optimize a reinforced concrete structure. For this, the Whale Optimization Algorithm (WOA) algorithm was used, an algorithm from the group of metaheuristic algorithms, which presents an easy computational implementation. As a study object, a frame structure adapted from a real reinforced concrete building was used, subjected to the dynamic action of artificially generated synoptic wind. The objective function is to reduce the volume of concrete of the structure. For that, the dimensions of the cross-sections were used as design variables, and the maximum displacement at the top imposed by the ASCE / SEI 7-10 standard as a lateral constraint, as well as the maximum story drift between floors. In addition to this structural optimization, it was also proposed the use and optimization of Tuned Mass Dampers (TMD), in different quantities, positions and parameters, improving the dynamic response of the reinforced concrete building. The results show that for this situation it was possible to reduce the concrete volume of the structure by approximately 24%, respecting the maximum limit of displacement at the top required by the standard. [ABSTRACT FROM AUTHOR]

Electricity outages can result in consequences for customers and cause disruptions that result in revenue loss, business productivity reduction, appliance damage, and compromised public safety. To minimize outage duration, locating faults quickly and accurately is crucial. Advanced fault management strategies leverage computational intelligence and deploy fault indicators (FIs) and sectionalizing switches (SSs) to locate faults and isolate faulty parts. Although FIs and SSs can improve distribution network reliability, deploying them at every node is impractical owing to cost and complexity. Therefore, it is important to optimize their placement in strategic locations. This research proposes a new optimization problem for the simultaneous placement of FIs and SSs. The aim is to minimize the cost of equipment placement, dispatching field crews, and service interruption. To solve the proposed formulation, the Backtracking Search Algorithm (BSA), a population-based meta-heuristic method, is used to identify optimal nodes for FIs and SSs placement. The performance of the BSA is evaluated through statistical tests, and the proposed strategy is shown to be effective compared to state-of-the-art methods. The results reveal a significant 7% reduction in device requirement, leading to a substantial 6.69% decrease in equipment expenditures and an overall decline in system costs compared to the established approach implemented in the test system. This research highlights the importance of optimization strategies for fault management systems and demonstrates the potential of meta-heuristic algorithms for solving complex problems. • A new optimization problem for FI, RCS, and MS placement in the distribution network. • A new solution strategy, such as BSA, is used to solve the optimization problem. • BSA shows robustness and superior performance compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The Golden Jackal Optimization (GJO) algorithm is a novel, nature-inspired optimization technique that has gained recognition as a highly promising metaheuristic due to its notable efficiency and adaptability. However, the GJO algorithm possesses several shortcomings, including inadequate exploitation ability, vulnerability to local optima, and imbalanced exploration-exploitation. Therefore, this paper presents an enhanced version of the named mGJO, tailored explicitly for addressing real-world engineering and feature selection problems. The mGJO incorporates four improvements to the standard GJO. To begin with, Chaos-OBL (COBL) based population initialization is proposed to increase diversity in population. Secondly, a nonlinear control parameter is proposed to regulate the exploration-exploitation transition. Thirdly, the original updating rule of GJO is combined with the ESQ mechanism to prevent the leader solutions from influencing the local optimal solutions. Finally, a new adaptive mutation strategy is presented to alter individuals and improve positions based on their fitness. To evaluate the efficiency of the suggested mGJO is assessed using CEC2005, CEC2017, CEC2020 and CEC2022 benchmark functions. The proposed mGJO is also applied to five common real-world engineering problems and is also applied to the feature selection problems to assess its performance. Furthermore, the proposed mGJO is compared with various advanced algorithms to evaluate its performance. Statistical analyses, including the Friedman rank test and Wilcoxon test, are applied to the experimental data to validate the superiority of the extracted results. The experimental results and the statistical analysis indicate the superiority of mGJO in terms of convergence rate, solution quality, and optimization accuracy. [ABSTRACT FROM AUTHOR]

In recent years, numerous meta -heuristic algorithms based on swarm intelligence have been proposed and widely popularized. Although algorithms are designed by some specific behaviors of organisms, their heuristic paradigms and constructed modules are similar, resulting in algorithms still suffer from imbalance between exploration and exploitation for complex optimization problems. Combining mathematical properties with a stochastic search process in meta -heuristic algorithms contributes to breaking the traditional single/dual population-based evolutionary paradigm and facilitating individuals forward optimization. Taking this as motivation, this article proposes a novel mathematics-based meta -heuristic algorithm, named Triangulation Topology Aggregation Optimizer (TTAO), for solving continuous optimization and engineering applications. The core of the proposed algorithm is based on similar triangular topology in mathematics. The TTAO algorithm contains two strategies, i.e., the generic aggregation and the local aggregation, which contributes to iteratively constructing multiple similar triangular topological units to balance the exploration and exploitation. The former generates new vertexes through positive information exchange between different triangular topological units. And the latter constructs new units at promising locations according to the local optimum vertex of each unit. The performance of the TTAO algorithm is evaluated in comparison with some competitive algorithms on CEC2017 functions for different dimensions and 8 real-world engineering problems. Moreover, the Wilcoxon rank sum test is used to verify the effectiveness of the proposed algorithm. The experimental results show that the TTAO algorithm, compared with 10 competitors, obtains 23, 23, and 22 best average results for 29 CEC2017 functions on 30, 50 and 100 dimensions, and its Wilcoxon rank sum test scores still rank first on three different dimensional cases. And more numerical results verify the outstanding optimization performance of the TTAO algorithm effectively. Source codes of TTAO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/136029-triangulation-topology-aggregation-optimizer. [ABSTRACT FROM AUTHOR]

Bioenergy is a sustainable alternative to conventional energy sources. However, production is limited by the complexities of the process and is also time consuming. Recent advancements in artificial intelligence and machine learning offer promising opportunities for streamlining bioenergy conversion processes. This paper aimed to develop a generic model for predicting biodiesel yield percentages from various biomass derived feedstocks through transesterification reactions. The biodiesel yield data and relevant features and properties of the feedstocks are collected from open literature sources. This work mainly focuses on establishing a generic model framework for biodiesel yield prediction. This involves data preprocessing techniques, machine learning model selection, and hyperparameter tuning. The grid search cross-validation was employed to optimize the CatBoost regressor, resulting in a prediction model with a root mean squared error value of 4.288. Moreover, the study conducted SHapley Additive exPlanations analysis to gain insights into the influence of individual data points on the prediction model. This analysis provided valuable insights into the impact of different variables on biodiesel yield prediction. Overall, this research presents a comprehensive approach to predicting biodiesel yield using machine learning techniques and highlights the effectiveness of the whale optimization algorithm for model optimization. The findings contribute to advancing the understanding of the factors influencing biodiesel production and provide valuable insights for optimizing the transesterification process. • Experimental data on biodiesel production using various feedstocks and catalysts were collected from the literature. • Various supervised machine learning algorithms were implemented and screened for optimizing and modeling of biodiesel production. • The categorical boosting regressor + grid search model predicted biodiesel yield with the RMSE value of 4.288. • Models were analyzed by SHap analysis to study the impact and correlating effects of features on biodiesel yield. • Screened supervised machine learning models were effective in predicting biodiesel yield accurately. [ABSTRACT FROM AUTHOR]

The slow dynamics response of a PEMFC to high-level load variation must be solved. Consequently, it is necessary to integrate the DC microgrid with battery storage banks and ultracapacitors. To guarantee the DC microgrid components: PV array, PEMFC, battery bank, and supercapacitor work effectively; energy management strategies (EMSs) are essential. The EMS distributes the load with the PV array, PEMFC, lithium-ion battery, and supercapacitor considering high efficiency and low H 2 consumption. An effective EMS using a recent Beluga Whale Optimization (BWO) was developed for sharing the load between the components of the DC microgrid. Sliding mode control (SMCS), classical equivalent consumption minimization strategy (ECMS) and other optimization algorithms such as White Shark Optimizer (WSO), Manta ray foraging optimization (MRFO), Harris Hawks Optimizer (HHO), soon eagle search (BES) and Artificial Hummingbird Algorithm (AHA) were considered for comparison with the suggested BWO. The results approved the superiority of the suggested ECMS-based BWO; the hydrogen consumption was reduced by 37.67 %, 46.4 %, 25.5 %, 32.58 %, 12.94 %, 12.1 %, and 9.67 % compared to SMCS, ECMS, WSO, MRFO, HHO, BES, and AHA, respectively. In addition, efficiency increased by 4.4 %, 13.49 %, 5.24 %, 10.51 %, 0.95 %, 4.52 %, and 0.32 % compared to SMCS, ECMS, WSO, MRFO, HHO, BES, and AHA, respectively. • New application for beluga whale optimization algorithm is proposed. • Thanks to proposed ECMS-based BWO, the performance is improved. • H2 consumption was reduced by 37.67 %, 46.4 %, 25.5 %, 32.58 %, 12.94 %, 12.1 %, and 9.67 % compared to SMCS, ECMS, WSO, MRFO, HHO, BES, and AHA. • Efficiency increased by 4.4 %, 13.49 %, 5.24 %, 10.51 %, 0.95 %, 4.52 %, and 0.32 % compared to SMCS, ECMS, WSO, MRFO, HHO, BES, and AHA, respectively. [ABSTRACT FROM AUTHOR]

• Multi-objective formulation including multiple criteria for hypothesis testing. • Estimation considering simultaneously multiple complex modelling and data aspects. • A metaheuristic and maximum log likelihood to test large numbers of hypothesis. • Comparative benchmarking including multiple data sources and published models. • Capabilities to capture more insights relative to current practice and methods. This paper focuses on the discrete choice estimation problem, which involves multiple objectives and testing a broad range of hypotheses that can affect both interpretability and prediction accuracy. Previous studies have proposed mathematical programming formulations to assist with hypothesis testing and estimation. However, there is limited knowledge regarding the effect of in- and out-of-sample model performance criteria during the search for parsimonious specifications. To address this knowledge gap, a multi-objective optimization framework is proposed, including both in-sample goodness-of-fit and out-of-sample predictive accuracy, to generate multiple unique specifications and perform extensive hypothesis testing considering simultaneously potential explanatory variables, their functional forms, nonlinearities, heterogeneous effects, and correlations. A metaheuristic was designed and implemented to solve the proposed multi-objective nonlinear mixed-integer mathematical programming problem. Experiments, including various datasets and discrete choices, were used to illustrate the efficacy of the proposed framework. The goal was to find specifications that are either similar or dominate those reported in literature, considering both interpretability and prediction accuracy. Important insights regarding potential explanatory factors and heterogeneous preferences, which were not reported in literature, were captured using the proposed framework. In addition, for one of the datasets used in this study, the proposed framework enabled the discovery of three distinct clusters considering specification type and model performance in terms of interpretability and prediction accuracy. For the given dataset, these clusters suggest that the proposed approach allowed extensive exploration of the data across different specification types. In addition, the Mixed-Logit models with correlated parameters were found to perform significantly better in terms of in-sample fit than those without correlation. Similarly, multinomial-Logit models showed the worst performance for the given dataset. In contrast, multinomial-Logit models provided superior out-of-sample fit relative to advanced specifications, which illustrates trade-offs between model in- and out-of-sample fitness. A comparative analysis, including multiple performance measures, was also conducted. The results suggest that model evaluation using in-sample Bayesian Information Criterion (BIC) and out-of-sample Mean Absolute Error (MAE), and in-sample BIC and out-of-sample Mean Squared Error (MSE) enables estimation of specifications with better in- and out-of-sample performance compared to those estimated using maximum log-likelihood and minimum number of model parameters. In addition, a mostly linear relationship was observed between in-sample and out-of-sample log-likelihood, indicating that the latter does not provide much additional information regarding prediction compared to the in-sample estimates. These results showed the value of using an optimization framework to support modelling decisions by enabling extensive hypothesis testing and including multiple performance criteria as well as complex data characteristics to discover important and reliable insights. [ABSTRACT FROM AUTHOR]

Residual drift of structural systems after seismic events can make buildings uninhabitable and may cause their collapse in aftershocks, as seen in the 2023 Turkey-Syria earthquake. Self-centering rocking-core systems can eliminate plastic deformation of structures after earthquakes. In the current study, a bi-rocking steel-braced frame was designed using the modified modal superposition (MMS) method. The optimal location of the secondary joint was found to minimize the base shear, overturning moment, peak floor acceleration and inter-story drift. Damage to non-structural components has been considered. The models included 12-, 18- and 24- story frames that were subjected to far-field (FF), near-field-pulse (NP) and near-field-no-pulse (N) ground motion records. The seismic records were scaled to the maximum credible earthquake level. Nonlinear time-history analyses were performed using OpenSees open-source finite element software. Additionally, post-tensioned cables and energy dissipation fuses as variable parameters were optimized using particle swarm optimization. The performance of seven metaheuristic algorithms was compared and the results showed that placing the secondary joint at the mid-height of the structure reduced all seismic demand. The results also suggest that the story moment and FF records were most sensitive to the location of the secondary joint. It was observed that frames with lower stiffness and yield strength of the damper exhibited better seismic performance. Based on this, corrective percentages have been proposed for the design of the cable and dampers. A self-centering ratio of 1.07 was suggested at the mid-height and 1.19 for the base damper. The colliding bodies optimization algorithm performed best for time-history analysis-based optimization. • Using a bi-rocking mechanism to reduce the higher mode effects of high-rise structures. • Designing the bi-rocking steel-braced frame with modified modal superposition method. • Optimization of the main design parameters of bi-rocking joints using metaheuristic algorithms. • Investigating the performance of the bi-rocking steel-braced frame under far- and near-field ground motions. [ABSTRACT FROM AUTHOR]

The Particle Swarm Optimization technique (PSO) is widely used in practical applications due to its flexibility and strong optimization performance. However, like other metaheuristic algorithms, PSO has limitations, such as a propensity to become trapped in local minima and an uneven distribution of effort between exploration and exploitation stages. A novel local search technique called QPSOL, based on PSO is the proposed solution to mitigate these issues. QPSOL aims to increase diversity and achieve a closer balance between the exploration and exploitation phases. The QPSOL incorporates a dynamic optimization strategy to enhance the method's efficiency. Unlike the novel local search strategy, which uses a new local search approach (LSA) to break out of local optima, QPSOL employs quadratic interpolation around the optimal search agent to enhance its exploitation capability and solution accuracy. These strategies complement each other and contribute to boosting PSO's convergence efficiency while seeking to balance exploration and exploitation. The proposed method is assessed using the IEEE CEC'2021 test suite, and its efficacy is evaluated against other metaheuristics and cutting-edge algorithms to determine its trustworthiness. The optimal parameters of three PV models are determined using the proposed technique and compared to different well-established algorithms. Systematic comparisons show that QPSOL is competitive with, and often outperforms, commonly used methods in research for predicting model parameters. [ABSTRACT FROM AUTHOR]

This study fixates on determining the optimum laser input parameters that simultaneously meet the desired kerf width and depth during CO2 laser cutting of various polymethylmethacrylate (PMMA) sheets. It has three contributions. The first is to model the cutting process of PMMA by polynomial curve fitting as a function of laser power, laser speed, and standoff distance. R-squared (R2), adjusted R2 and root-mean-square error (RMSE) criteria were taken into account to measure the performance of the proposed model. The effect of laser parameters on the process is investigated by analysis of variance (ANOVA) and sensitivity analysis. The second is to optimize the derived nonlinear regression models using genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA) and ant lion optimization (ALO) metaheuristic methods and compare the performances of the algorithms. The third is to compare the adequacy of the optimization process with the artificial neural network (ANN). The investigations exhibited that the best-fitting polynomials are obtained with the R2 and adjusted R2 values of over 99% and 97%, respectively. ANOVA and sensitivity test revealed that the sensitivity of the laser power, which is the most effective parameter, was 150 at low powers and decreased to 0 as the power value increased. When the nozzle distance is 4.1, the proposed metaheuristics gave effective and sufficiently accurate results. PSO stood out in terms of both best cost value (3.49 × 10–8) and relative error value (0.19%). The relative error of the ANN was found as 3% in terms of kerf depth. [ABSTRACT FROM AUTHOR]

[Display omitted] • A hybrid algorithm for reliable and efficient parameter estimation of the PV model. • Significant advancements are achieved in methodology and objective function design. • Performance of the algorithm is analyzed with partial shading and noise effects. • Proposed approach achieves enhancements in the overall performance and accuracy. • It outperforms other algorithms in terms of accuracy, consistency, and convergence. A reliable methodology is essential for accurately estimating the parameters of PV models, enabling reliable performance evaluations, effective control studies, accurate analysis of partial shading effects, and optimal optimization of Photovoltaic (PV) systems. It ensures that the obtained parameters reflect the true characteristics of the PV system, leading to more accurate and reliable results in various applications. The existing literature extensively explores the utilization of powerful Metaheuristic Algorithms (MAs) to address the complex constrained optimization problem in PV systems and achieve optimal solutions. However, it is important to note that a significant portion of these MAs primarily concentrates on the development of methodologies, often overlooking the design of the objective function tailored for PV systems. This oversight has created a theoretical gap in this research domain, underscoring the necessity for additional exploration and investigation to address this limitation. To address the existing theoretical gap, this study focused on developing an objective function that accurately estimates the initial root parameters of Photovoltaic (PV) models. This objective function was designed by incorporating the first-order Berndt-Hall-Hall-Hausman (BHHH) numerical method, along with the non-linear damping parameter of the Levenberg-Marquardt technique (LM). By implementing this approach, the study aimed to significantly improve the precision and reliability of estimating the initial root parameters in PV models, effectively filling the theoretical void in this specific research area. Then in terms of methodology, the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is combined with the Sine-Cosine mutualism phase of Symbiotic Organisms Search (SOS) for efficiently estimating the unknown parameters of PV models. The keystone of EHGSO in terms of methodology enhances exploration at the beginning of optimization and intensifies exploitation in later iterations. The proposed EHGSO methodology based on the adaptive damping BHHH technique (EHGSO AdBHHH) is tested on Single Diode (SD), and Double Diode (DD) PV models using actual experimental data. EHGSO AdBHHH exhibits outstanding accordance with attained experimental data compared with other algorithms, and its superiority is validated using several statistical criteria. [ABSTRACT FROM AUTHOR]

Optical Wireless Communication (OWC) is being explored for application in the next-generation broadcasting networks, where possessing accurately determined user locations becomes increasingly important. Received signal strength (RSS) Visible Light Positioning (VLP)-based localisation systems aim to deliver these centimetre-level location data at a low cost by featuring but a single photodiode (PD). Maximising the VLP accuracy requires optimising the LED transmitter locations, which is missing currently. An evolutionary optimisation algorithm is proposed to determine the optimal LED locations and the associated positioning error values for various configurations. The sensitivity of the planning on the number of VLP-enabled LEDs, the LEDs’ characteristics, the room dimensions and the positioning parameters is investigated. Experimental data, i.e., two datasets with 1572 measurement points each, serve to validate the simulations. [ABSTRACT FROM AUTHOR]

The use of metaheuristic optimization techniques in obtaining the optimal weights of neural network model for the time series was the main part of this research. The three optimization methods used as experiments were genetic algorithm (GA), particle swarm optimization (PSO), and modified bee colony (MBC). Feed forward neural network (FFNN) was the neural network (NN) architecture chosen in this research. The limitations and weaknesses of gradient-based methods for learning algorithm inspired some researchers to use other techniques. A reasonable choice is non-gradient based method. Neural network is inspired by the characteristics of creatures. Therefore, the optimization techniques which are also resemble the patterns of life in nature will be appropriate. In this study, various scenarios on the three metaheuristic optimization methods were applied to get the best one. The proposed procedure was applied to the rainfall data. The experimental study showed that GA and PSO were recommended as optimization methods at FFNN model for the rainfall data. [ABSTRACT FROM AUTHOR]

Longitudinal barriers are considered as passive safety systems designed to shield hazards located at roadsides against motor vehicle impacts. Since these barriers are manmade obstacles, they also pose a threat to drivers using the road. Recent motorcyclist accidents with longitudinal barriers have proven that a particular barrier successfully protecting vehicle occupants may wound or kill motorcyclists due to its components. For this reason, sharp and blunt edges in steel longitudinal barrier parts, such as posts are usually shielded against contact from unprotected motorcyclists during a high-speed impact event. In recent years, more longitudinal barriers have been designed with motorcyclists in mind and these motorcycle protection barriers have become wide spread especially on urban high-speed roads. However, since the development of these barriers are fairly new compared to conventional longitudinal barriers, there is limited guidance on their design criteria, such as thickness, geometry, connections. For this purpose, this paper intends to provide an artificial neural network metamodeling-based design optimization methodology to an existing continuous motorcycle protection barrier design to make it more competitive in terms of weight and thus, cost. As a result of this study, the optimized barrier has become 34% more economical compared to its original design while its protection level remained intact. [ABSTRACT FROM AUTHOR]

This paper introduces a new meta-heuristic algorithm called Thieves and Police Algorithm (TPA), for solving probabilistic optimization problems. The proposed algorithm is designed in five different phases each following a certain objective. In this algorithm, conventional matters between the thieves' team and the police are inspired as a social phenomenon. Motivation of thieves for stealing more properties is utilized in this algorithm to seek the optimum solution, and police track that result in fear and no risk taking of the thieves to enter areas under police surveillance is inspired to omit areas with weaker solutions. Algorithm optimization procedure ends with apprehending the leader thief by police as the optimum solution. The performance of the TPA is evaluated in terms of local optima avoidance, exploration, exploitation, effectiveness, and convergence properties using a set of classical and modern test functions. Comparing the outcomes of other meta-heuristic optimization algorithms, the successful performance of the TPA in solving different optimization problems is confirmed. Moreover, the efficiency of the TPA in terms of solving problems with probabilistic nature is assessed by solving an optimization problem in power engineering area, known as Probabilistic Power Flow (PPF), in a Micro-Grid with renewable energy resources. Based on the outcomes, the proficiency of the proposed algorithm is confirmed by solving the PPF problem and producing timely acceptable results. Hence, the TPA algorithm is suggested to solve complicated problems, with probabilistic nature, in the optimization field and other optimization problems. [ABSTRACT FROM AUTHOR]

As distribution networks operate in their majority in a radial way, this condition becomes one of the most important constraints of distribution network reconfiguration (DNR) problem. There are diverse ways to represent this in DNR, but a lack of studies that provide an analysis of its behavior is noted, specially regarding its influence in metaheuristics and bio‐inspired metaheuristics. This paper presents a discussion between two alternatives to represent this in DNR in conjunction with three new bio‐inspired metaheuristics. One is based on the formation of the incidence matrix of the system and its determinant; the other is a modified approach of a method that establishes a set of forbidden switches through the analysis of the system fundamental loops. To solve DNR, three new bio‐inspired metaheuristics are presented: monarch butterfly optimization (MBO), gray wolf optimizer (GWO), and marine predators algorithm (MPA). A comparison is performed with selective particle swarm optimization (SPSO). To test the alternatives, three systems are used (33‐, 69‐, and 84‐ bus), with results showing the influence of the radiality approach in the results of bio‐inspired metaheuristics. [ABSTRACT FROM AUTHOR]

This paper presents a powerful swarm intelligence metaheuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization Algorithm. The original Cat Swarm Optimization suffers from the shortcoming of "premature convergence," which is the possibility of entrapment in local optima which usually happens due to the off balance between exploration and exploitation phases. Therefore, the proposed algorithm suggests a new method to provide a proper balance between these phases by modifying the selection scheme and the seeking mode of the algorithm. To evaluate the performance of the proposed algorithm, 23 classical test functions, 10 modern test functions (CEC 2019) and a real-world scenario are used. In addition, the dimension-wise diversity metric is used to measure the percentage of the exploration and exploitation phases. The optimization results show the effectiveness of the proposed algorithm, which ranks first compared to several well-known algorithms available in the literature. Furthermore, statistical methods and graphs are also used to further confirm the outperformance of the algorithm. Finally, the conclusion and future directions to further improve the algorithm are discussed. [ABSTRACT FROM AUTHOR]

Neutrosophic set is a branch of neutrosophy concerned with nature, the genesis, and breadth of impartialities, and also their interaction with various mental spectra. Neutrosophic sets constitute relatively new expansions of intuitionistic fuzzy. In a short period of time, numerous researchers have accepted neutrosophic reasoning. Many researchers have linked neutrosophic science and metaheuristics in various ways over the last ten years. Metaheuristic research has attracted a great attention throughout the literature, which covers methodologies, apps, comparative analysis; due to its higher intensities and fruitful implementations. Metaheuristic algorithms are used to introduce the best or the optimum solutions to a lot of optimization problems due to the behavior of these algorithms inspired by Nature and its ability to adapt to problems, as well as the possibility of integrating more than one algorithm to reach the best solutions. Based on the previous reason, many researchers used these algorithms with neutrosophic science to present many platforms in the recent years, which was the motivation to introduce this survey paper. This paper is introduced to cover the publications from 2010 to 2021 in order to draw a comprehensive picture of metaheuristic research integrated with neutrosophic theory. [ABSTRACT FROM AUTHOR]