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This paper provides a systematic review of the Genetic Algorithm (GA)s proposed to solve planning and scheduling problems in Engineer-To-Order (ETO) contexts. Our review focuses on how the key characteristics of ETO projects affect both the problem studied and the GA algorithmic features. Typical ETO projects consist of one-of-a-kind products with complex structures and uncertain designs. A deep analysis of the papers published between 2000 and 2022 enables identifying 10 main characteristics of ETO projects, six activity types, 10 decision types, eight groups of constraints, and 10 optimisation objectives. Our study shows that none of the reported papers integrates all 10 ETO characteristics. The less studied ETO characteristics are incorporating design and engineering information in the problem definition and the design uncertainty. Our review also identifies 10 recurrent encoding formats and emphasises the most frequently used genetic operators. We observed that most planning and scheduling problems consider objectives and decisions related to product customisation or supply chain configuration yielding multi-objective problems. Most multi-objective GAs use a weighted sum or are based on NSGAII. Diversity maintenance methods, adaptive and parameter tunning mechanisms, or hybridisation with machine learning models are still not used in this context. A systematic review of genetic algorithms dedicated to industrial planning and scheduling Analysis on how the characteristics of ETO projects impact the design of genetic representation and operators Recommendation on approaches employed to reach high-quality solutions [ABSTRACT FROM AUTHOR]

A two-dimensional (2D) piston electro-hydraulic pump has been proposed further to enhance the power density of the electro-hydraulic pump. The 2D piston pump, characterized by high power density and a slender shape, is embedded within the stator of the motor in a co-rotor configuration where the piston and the motor's rotor are in tandem. The intimate design of the hydraulic pump and the motor results in a coupling between the two, with intricate relationships and influences existing between the geometric parameters of the piston pump and the dimensions of the motor's rotor. Based on the operational requirements and structure of the 2D piston pump, a permanent magnet synchronous motor (PMSM) designed for use with a 2D piston electro-hydraulic pump is developed. This study examines the impact of the motor's stator iron core geometric parameters on both the electromagnetic and mechanical properties of a PMSM and completes the necessary performance validations. The optimization objectives of the motor are determined through an analysis of the influence of the key parameters of the rotor and stator on torque, torque ripple, and motor loss. A surrogate optimization model is constructed using a metamodel of optimal prognosis (MOP) to optimize the torque, torque ripple, and motor loss. Evolutionary genetic algorithms are utilized to achieve the multi-objective optimization design. A finite element simulation is used to compare the electromagnetic performance of the initial motor and optimal motor. Based on the optimal motor parameters, a 2.5 kW motor prototype is manufactured, and the experimental results validate the feasibility and effectiveness of the motor design and optimization. [ABSTRACT FROM AUTHOR]

The seismic design of structures is an emerging practice in earthquake-resistant construction. Therefore, using energy-dissipation devices and optimizing these devices for various purposes are important. Evolutionary computation, nature-inspired, and meta-heuristic algorithms have been studied more in recent years for the optimization of these devices. In this study, the development of evolutionary algorithms for seismic design in the context of multi-objective optimization is examined through bibliometric analysis. In particular, evolutionary algorithms such as genetic algorithms and particle swarm optimization are used to optimize the performance of structures to meet seismic loads. While genetic algorithms are used to improve both the cost and seismic performance of the structure, particle swarm optimization is used to optimize the vibration and displacement performance of structures. In this study, a bibliometric analysis of 661 publications is performed on the Web of Science and Scopus databases and on how the research in this field has developed since 1986. The R-studio program with the biblioshiny package is used for the analyses. The increase in studies on the optimization of energy dissipation devices in recent years reveals the effectiveness of evolutionary algorithms in this field. [ABSTRACT FROM AUTHOR]

The variable geometry methods currently used in combined-cycle gas turbines are compressor variable inlet guide vanes (VIGV) or power turbine variable area nozzles (VAN). On this basis, this study presents the optimal variable geometry control strategy for compressor and power turbine combined adjustment ( (VIGV + VAN) opt ) using the Differential Evolutionary Algorithm with the LM2500+ gas turbine. The aim is to further improve the part-load performance of the combined-cycle gas turbine. Firstly, a part-load performance prediction model for variable geometry gas turbines is established based on the component method. Subsequently, a variable geometry gas turbine part-load performance optimization model is developed by combining the Differential Evolution Algorithm. Finally, the optimum combination of stagger angles for the compressor inlet vane and power turbine nozzle is calculated at each part-load condition. Compared to the VIGV and VAN control strategies, the (VIGV + VAN) opt control strategy proposed in this paper shows a higher stability margin and better economy. The (VIGV + VAN) opt control strategy maintains a constant exhaust temperature within a part load range from 20% to 100% with the stability margin exceeding 14%. In comparison with the VAN control strategy, the fuel flow rate decreases by 1.152% at 45% relative load power and by 3.435% at 20.0% relative load power with the (VIGV + VAN) opt control strategy. [ABSTRACT FROM AUTHOR]

The atmosphere is one of the game elements that can significantly influence player's emotions. However, creating an immersive atmosphere that effectively influences player emotions poses several challenges, necessitating the utilization of various elements, such as audio-visual coordination and gameplay design. This paper introduces a general framework for procedurally generating dungeons with joyful and horror atmospheres in games, providing an abstract perspective to address these challenges. The proposed framework introduces a categorization system for game elements based on their role within the game. Leveraging this categorization, the Comprehensive Arrangement of Game Elements (CAGE) pattern is introduced, which facilitates the appropriate placement of elements within the dungeon environment. Subsequently, the General Framework for Generating Dungeons with Atmosphere (GFGDA) is employed to procedurally create the dungeon using the Feasible–Infeasible Two-Population (FI-2Pop) algorithm. To enhance gameplay experience, similar elements in the dungeon environment that impact gameplay are grouped and their coordination is evaluated by creating a graph based on the CAGE pattern. The transition and coordination of audio-visual elements along the path between these impactful elements are assessed in order to generate an immersive atmosphere within the dungeon. To ensure diversity, examining the variety of dungeons generated over 100 runs demonstrates that our method consistently produces distinct results in each iteration. Moreover, two comparative studies were conducted, one with 51 volunteers and another with 10 volunteers. In the first study, the Game Experience Questionnaire (GEQ) was utilized to assess the emotional impact of dungeons generated by our method. These were compared to dungeons created using a uniform random approach, alongside relevant research. The results suggest that our method significantly influences player emotions across the four components of the GEQ—sensory and imaginary immersion, flow, negative effects, and challenge—when compared to dungeons generated by the uniform random approach and another researched method. In another study, the emotional impact of two dungeons, one generated with joyful elements and the other with eerie elements, was evaluated using the GEQ. The findings indicate significant differences between the two components of the GEQ—tension and positive effects—when players interacted with the level containing joyful elements compared to the one with eerie elements. [ABSTRACT FROM AUTHOR]

In a seminal paper in 2013, Witt showed that the (1+1) Evolutionary Algorithm with standard bit mutation needs time (1 + o (1)) n ln n / p 1 to find the optimum of any linear function, as long as the probability p 1 to flip exactly one bit is Θ (1) . In this paper we investigate how this result generalizes if standard bit mutation is replaced by an arbitrary unbiased mutation operator. This situation is notably different, since the stochastic domination argument used for the lower bound by Witt no longer holds. In particular, starting closer to the optimum is not necessarily an advantage, and OneMax is no longer the easiest function for arbitrary starting positions. Nevertheless, we show that Witt's result carries over if p 1 is not too small, with different constraints for upper and lower bounds, and if the number of flipped bits has bounded expectation χ . Notably, this includes some of the heavy-tail mutation operators used in fast genetic algorithms, but not all of them. We also give examples showing that algorithms with unbounded χ have qualitatively different trajectories close to the optimum. [ABSTRACT FROM AUTHOR]

Efficiency and accuracy have been challenging in the design optimisation process driven by building simulation. The literature review identified the limitations of previous studies, prompting this study to explore the performance of single-objective versus multi-objective efficiency and accuracy on equivalent problems based on control variables and to consider more algorithmic options for a broader range of designs. This study constructed a comparative energy-related experiment whose results are in the same unit, either as a single-objective optimisation or split into two objectives. The project aims to reduce annual energy consumption and increase solar utilisation potential. Our approach focuses on the use of a surrogate modelling algorithm, Radial Basis Function Optimisation Algorithm (RBFOpt), with its multi-objective version RBFMOpt, to optimise the energy performance while quickly identifying new energy requirements for an iterative office building design logic, contrast to traditional genetic-algorithm-driven. In addition, the research also conducted a comparative study between RBFOpt and Covariance Matrix Adaptation Evolutionary Strategies (CMAES) in a single-objective comparison and between RBFMOpt and Nondominated Sorting Genetic Algorithm II (NSGA-II) in a multi-objective optimisation process. The comparison of these sets of Opt algorithms with evolutionary algorithms helps to provide data-driven evidence to support early design decisions. [ABSTRACT FROM AUTHOR]

To advance wave energy devices towards commercialization, it is essential to optimize their design to enhance system performance. Additionally, a thorough economic evaluation is crucial for making these technologies competitive with other renewable energy sources. This study focuses on the techno-economic optimization of an innovative inertial system, the so-called SWINGO system, which is based on gyropendulum technology. SWINGO stands out due to its high energy efficiency in multi-directional installation sites, where wave directions vary significantly throughout the year. The study introduces the application of a multi-objective Evolutionary Algorithm (EA), specifically the Non-dominated Sorting Genetic Algorithm II (NSGA-II), to optimize the techno-economic performance of the SWINGO system. This approach aims to identify optimal design parameters that maximize energy extraction while considering economic viability. By deriving a Pareto frontier, a set of optimal devices is selected for further analysis. The performance of the SWINGO system is also compared to an alternative (mono-directional) inertial wave energy converter, the Inertial Sea Wave Energy Converter (ISWEC), to highlight the differences in techno-economic outcomes. Both systems are evaluated at two different installation sites: Pantelleria island and the North Sea in Denmark, with a focus on the directional wave scatter at each location. [ABSTRACT FROM AUTHOR]

The pursuit of studying the quadratic minimum spanning tree (QMST) problem has captivated numerous academics because of its distinctive characteristic of taking into account the cost of interaction between pairs of edges. A QMST refers to the minimum spanning tree, which is a graph that is both acyclic and minimally connected. It also includes the interaction cost between a pair of edges in the minimum spanning tree. These interaction costs can occur between any pair of edges, whether they are adjacent or non-adjacent. In the QMST problem, our objective is to minimize both the cost of the edges and the cost of interactions. This eventually classifies the task as NP-hard. The interaction costs, sometimes referred to as quadratic costs, inherently exhibit a contradictory relationship with linear edge costs when solving a multi-objective problem that aims to minimize both linear and quadratic costs simultaneously. This study addresses the bi-objective adjacent only quadratic minimum spanning tree problem (AQMSTP) by incorporating the uncertain nature of the linear and quadratic costs associated with the problem. The focus is on the interaction costs between adjacent edges. Consequently, we have introduced a multi-objective problem called the uncertain adjacent only quadratic minimum spanning tree problem (mUAQMSTP) and formulated it using the uncertain chance-constrained programming technique. Afterwards, two MOEAs—non-dominated sorting genetic algorithm II (NSGAII) and duplicate elimination non-dominated sorting evolutionary algorithm (DENSEA)—and the traditional multi-objective solution approach, the global criterion method, are employed to solve the deterministic transformation of the model. Finally, we provide a suitable numerical illustration to substantiate our suggested framework. [ABSTRACT FROM AUTHOR]

Featured Application: This work contributes to different industrial sectors through a computer tool that optimizes the layout of a robotic cell, which allows reducing both production times and energy consumption, mainly. The design of the work area of a robotic cell is currently an iterative process of trial and improvement, where, in the best cases, the user places the workstations and robotic manipulators in a 3D virtual environment to then semi-automatically verify variables such as the robot's reach, cycle time, geometric interferences, and collisions. This article suggests using an evolutionary computation algorithm (genetic algorithm) as a tool to solve this optimization problem. Using information about the work areas and the robot's reach, the algorithm generates an equipment configuration that minimizes the cell area without interference between the stations and, therefore, reduces the distances the robotic manipulator must travel. The objective is to obtain an optimized layout of the workstations and to validate this optimization by comparing the transfer times between stations with the actual times of an existing screwdriving cell. As a result, the transfer time was reduced by 9%. It is concluded that the algorithm can optimize the layout of a robotic cell, which can lead to significant improvements in efficiency, quality, and flexibility. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II (Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II) for solving complex multiobjective optimization problems, with a particular focus on robotic leg-linkage design. The study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II, aiming to enhance the efficiency and precision of the optimization process. Through a series of empirical experiments and algorithmic analyses, the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods, underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational demands. The methodology encompasses a detailed exploration of the algorithm’s configuration, the experimental setup, and the criteria for performance evaluation, ensuring the reproducibility of results and facilitating future advancements in the field. The findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design optimization. By bridging the gap between complex optimization challenges and achievable solutions, this research contributes valuable insights into the optimization domain, offering a promising direction for future inquiries and technological innovations. [ABSTRACT FROM AUTHOR]

To address the symmetry-related resilience issues of stations and lines in urban rail transit networks, we propose a two-stage robust optimization-based approach for urban rail transit network planning. In this context, resilience is conceptualized as the ability of the network to maintain its operational symmetry under normal and disruptive conditions. Firstly, we used passenger flow distributions as decision variables to construct a two-stage symmetry-based urban rail transit network planning model, aiming to simultaneously minimize the total cost and total operating time of the network while preserving its functional symmetry. Secondly, we designed a hybrid evolutionary algorithm with chromosomes having a two-layer encoding structure, where the Niched Pareto Genetic Algorithm served as the main algorithmic framework, and a Large Neighborhood Search mechanism was designed to optimize the connectivity gene layer of individuals, ensuring the symmetry of network connectivity. Finally, we conducted computational verification on randomly generated instances to confirm the effectiveness of the model and algorithm. The experimental results demonstrated that our method could find two sets of Pareto optimal solutions for cost preference and time preference, thereby preserving the operational symmetry of the network under normal and damaged conditions, as well as reducing the total operating time. This effectively improved the overall efficiency and resilience of the network. Our designed hybrid evolutionary algorithm converged to satisfactory objective values in the early iterations, exhibiting strong search and optimization performance and effectively solving the two-stage symmetry-based urban rail transit network planning model. [ABSTRACT FROM AUTHOR]

Learners' emotional states might change during the learning process, and unpredictable variations of a person's emotions raise the demand for regular assessment of feelings during learning. In this paper, an AI-based decision framework is proposed and implemented for e-learning systems that identify suitable micro-brake activities based on the learner's emotional state through an evolutionary genetic algorithm to change learner's mood and increase learning performance. This proposed framework was tested using a case study of English as a second language learner during one semester. The students were divided into two groups of participants (each group containing twenty students, forming a total of 40 students). The results of this study demonstrated the importance of learners' emotions in their learning performance and proved the effectiveness of our proposed framework and the success of the recommended micro-break activities chosen based on learners' emotions and preferences. The findings of this study have important practical implications in designing adaptive e-learning systems and learning management systems such as Moodle. They also contribute to theoretical implications in the field of AI and learner emotions by suggesting a novel approach to identifying, categorizing, and offering a learning path that can cater to the needs of individual learners. [ABSTRACT FROM AUTHOR]

Accurate diagnosis and effective treatment of thyroid conditions, such as hypothyroidism and hyperthyroidism, are crucial due to their wide-ranging symptoms and consequences. However, conventional back-propagation neural networks have limitations, including slow convergence and susceptibility to local minima. To deal with these problems, the paper suggests an integrated strategy that includes back-propagation (BP) and genetic algorithms (GA). The suggested method uses evolutionary algorithms to investigate various weight combinations and back-propagation to modify weights in response to the discrepancy between expected and actual results. The study evaluated the method using three steps of experimentation, including network building, local search and optimization, and evaluation, on a real-world dataset of thyroid illnesses. The obtained results are very satisfying and promising, indicating that the MLP-GA/BP model is robust enough to detect and categorize thyroid disorders effectively and efficiently. This makes it a reliable diagnostic tool for medical practitioners, allowing them to effectively diagnose and treat patients with thyroid diseases. [ABSTRACT FROM AUTHOR]

Machine configuration is a crucial strategic decision in designing a flow shop system (FSS) and directly affects its performance. This involves selecting device suppliers and determining the number of machines to be configured. This study addresses a bi-objective optimization problem for an FSS that considers repair actions and aims to determine the most suitable machine configuration that balances the production reliability and purchase cost. A nondominated sorting genetic algorithm II (NSGA-II) is used to determine all the Pareto solutions. The technique for order preference by similarity to an ideal solution is then used to identify a compromise alternative. It is necessary to assess the production reliability of any machine configuration identified by the NSGA-II. The FSS under the machine configuration is modeled as a multistate flow shop network, and Absorbing Markov Chain and Recursive Sum of Disjoint Products are integrated into the NSGA-II for reliability evaluation. The experimental results of solar cell manufacturing demonstrate the applicability of the proposed hybrid method and validate the efficiency of the NSGA-II compared with an improved strength Pareto evolutionary algorithm. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has enabled several optimization techniques for structural design, including machine learning, evolutionary algorithms, as in the case of genetic algorithms, reinforced learning, deep learning, etc. Although the use of AI for weight optimization in steel and concrete buildings has been extensively studied in recent decades, multi-objective optimization for reinforced concrete (RC) and steel buildings remains challenging due to the difficulty in establishing independent objective functions and obtaining Pareto fronts. The well-known Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is an efficient genetic algorithm approach for multi-objective optimization. In this work, the NSGA-II approach is considered for the multi-objective structural optimization of three-dimensional RC buildings subjected to earthquakes. For the objective of this study, two function objectives are considered: minimizing total cost and the probability of structural failure, which are obtained via several nonlinear seismic analyses of the RC buildings. Beams and columns' cross-sectional dimensions are selected as design variables, and the Mexican Building Code (MBC) specifications are imposed as design constraints. Pareto fronts are obtained for two RC-framed buildings located in Mexico City (soft soil sites), which demonstrate the efficiency and accuracy of NSGA-II for structural optimization. [ABSTRACT FROM AUTHOR]

An important part of UAV technological development consists of improvements in the scope of path planning. Different choices can be made in path planning according to operational priorities, it may be preferred to reach the destination as fast as possible or to increase the airtime by compromising speed. For every speed and altitude that the UAV can fly; fuel data of cruise, climb and descent phases are used in the path planning algorithm. Thus, economical and airtime-maximizing paths could be produced on the basis of performance characteristics compatible with the kinematic constraints customized for the UAV. In this study, Cellular (cGA) and Segmented Cellular Genetic Algorithm (scGA) are proposed. The novel overprotective algorithm which has a fixed initial population and segmented chromosome structure achieves a high convergence speed to optimal solution and can generate paths which have 5.2 times higher fitness value on average compared with a conventional Genetic Algorithm (GA). It has been seen that scGA improves the initial population in terms of the best solutions 1.9 times and the general population 5.8 times better compared with GA. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for solving a real‐world timetabling problem at Mandarine Academy. The primary motivation for this work is to provide an automated professional course scheduling tool to replace the time‐consuming task of manually creating timetables that are constantly incorrect. Following a review of both scientific literature and company requirements, a mathematical model of the problem is provided, which includes 18 constraints (hard/soft) and five objectives, two of which are competing. We test a handful of multi‐objective evolutionary algorithms (MOEA's) starting with the non‐dominated sorting genetic algorithm (NSGA II and NSGA III), the multi‐objective evolutionary algorithm based on decomposition (MOEA/D), the indicator‐based evolutionary algorithm and finally the strength Pareto evolutionary algorithm. Two custom genetic operators (mutation and crossover) are proposed and compared to conventional operators (PMX and swap mutation). To obtain elite configurations, a tuning phase involving all of the aforementioned algorithms is carried out. Experiments were divided by problem size, with three to five objectives tested. Experiments include the use of real‐world data from the company's catalog. This dataset was made available to the scientific community to serve as a testing ground for professional course scheduling, an underexploited field of scheduling. We discuss findings, including a comparison of each algorithm's performance using various metrics, as well as convergence graphs and population evolution. [ABSTRACT FROM AUTHOR]

This paper introduces a cooperative approach of a swarm intelligence algorithm and a linear programming solver to solve the capacitated facility location problem (CFLP). Given a set of potential locations to open facilities, the aim in CFLP is to find the minimum cost, which is the sum of facility opening costs and transportation costs. The developed solution strategy decomposes CFLP into two sub-problems. The former sub-problem has a binary domain. Although most of the swarm intelligence algorithms employ additional procedures such as sigmoid function to deal with binary domains, the proposed algorithm does not require for such methods. An adaptive mutation operator enhances this algorithm. The aim of the latter sub-problem is to generate a policy that optimally assigns customers to the opened facilities. In this regard, the generated binary vectors by the proposed algorithm are passed to a solver to optimise the generated linear model. Commonly used instances available in the literature are solved by the proposed strategy. Comprehensive experimental study includes comparisons with the sate-of-the-art. According to the statistically verified results, the proposed strategy is found as promising in solving CFLP. [ABSTRACT FROM AUTHOR]

AbstractThe structure for optimization is a composite cylinder subjected to hydrostatic pressure. The customization is in the method of incorporating the balanced laminate constraint using a while loop which not only improves buckling resistance but also drastically reduces the computation time. The formulation of the objective function is carried out in a novel way. The third customization is in considering geometric variables along with layup in optimization to arrive at the most optimal configuration. The results suggest a significant increase in buckling load ranging from 61.3% to 92.2% and a reduction by about 76.3% in time required for optimization. [ABSTRACT FROM AUTHOR]

Biomass supply chain (BSC) activities have caused social and environmental disruptions, such as climate change, energy security issues, high energy demand, and job opportunities, especially in rural areas. Moreover, different economic problems have arisen globally in recent years (e.g., the high costs of BSC logistics and the inefficiency of generating bioenergy from low-energy-density biomass). As a result, numerous researchers in this field have focused on modeling and optimizing sustainable BSC. To this end, this study aims to develop a multi-objective mathematical model by addressing three sustainability pillars (economic cost, environmental emission, and job creation) and three decision levels (i.e., strategic (location of facilities), tactical (type of transportation and routing), and operational (vehicle planning). A palm oil BSC case study was selected in the context of Malaysia in which two advanced evolutionary algorithms, i.e., non-dominated sorting genetic algorithm II (NSGA-II) and Multiple Objective Particle Swarm Optimization (MOPSO), were implemented. The study results showed that the highest amounts of profit obtained from the proposed supply chain (SC) design were equal to $13,500 million and $7000 million for two selected examples with maximum emissions. A better target value was achieved in the extended example when 40% profit was reduced, and the minimum emissions from production and transportation in the BSC were attained. In addition, the results demonstrate that more Pareto solutions can be obtained using the NSGA-II algorithm. Finally, the technique for order of preference by similarity to the ideal solution (TOPSIS) was adopted to balance the optimum design points obtained from the optimization algorithm solutions through two-objective problems. The results indicated that MOPSO worked more efficiently than NSGA-II, although the NSGA-II algorithm succeeded in generating more Pareto solutions. [ABSTRACT FROM AUTHOR]

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In this paper, we aim to enhance genetic algorithms (GAs) by integrating a dynamic model based on biological life cycles. This study addresses the challenge of maintaining diversity and adaptability in GAs by incorporating stages of birth, growth, reproduction, and death into the algorithm's framework. We consider an asynchronous execution of life cycle stages to individuals in the population, ensuring a steady-state evolution that preserves high-quality solutions while maintaining diversity. Experimental results demonstrate that the proposed extension outperforms traditional GAs and is as good or better than other well-known and well established algorithms like PSO and EvoSpace in various benchmark problems, particularly regarding convergence speed and solution qu/ality. The study concludes that incorporating biological life-cycle dynamics into GAs enhances their robustness and efficiency, offering a promising direction for future research in evolutionary computation. [ABSTRACT FROM AUTHOR]

In this paper, with the development of an intelligent power system idea, sustainable energy sources were increasingly deployed, including transmission and distribution systems networks. As a result, optimal use of cascaded H-bridge inverter topologies (MLIs) and power distribution operations is critical for long-term power generation. Traditionally, selective harmonics reduction models must be performed to achieve the optimal switching frequency of multilevel inverters. This research aims to determine the switching frequency for wind-incorporated multilevel inverters to reduce overall harmonic components used in grid applications. This research adds towards the best possible solution by employing multiple newly established adaptive optimization techniques: MNSGA-II and salp swarm. The well-known genetic algorithm and particle swarm optimization are used for the wind-tied multilevel inverters optimization issue. Sevenlevel, eleven-level, and fifteen-level MLIs were employed to reduce overall harmonic distortion. The reliability and convergence rate of simulated data with various modulation indices for seven-, eleven-, and fifteen-level MLIs are obtained and compared. Models are developed based on MATLAB Simulink and are used to validate quantitative measurements. [ABSTRACT FROM AUTHOR]

The objective of this study is to investigate the synergistic integration of machine learning and evolutionary algorithms for the discovery of equivalent morphologies exhibiting analogous behavior within the domain of composite materials. To pursue this objective, two comprehensive databases are meticulously constructed. The first database encompasses randomly positioned inclusions characterized by varying volume fractions and contrast levels. Conversely, the second database comprises microstructures of diverse shapes, such as elliptical, square, and triangular, while maintaining consistent volume fraction and contrast values across samples. Label assignment for both databases is conducted using a finite-element-method-based computational tool, ensuring a standardized approach. Machine learning techniques are then applied, employing distinct methodologies tailored to the complexity of each database. Specifically, an artificial neural network ANN model is deployed for the first database due to its intricate parameter configurations, while an eXtreme Gradient Boosting XGBoost model is employed for the second database. Subsequently, these developed models are seamlessly integrated with a genetic algorithm, which operates to identify equivalent morphologies with nuanced variations in geometry, volume fraction, and contrast. In summation, the findings of this investigation exhibit notable levels of adaptation within the discovered equivalent morphologies, underscoring the efficacy of the integrated machine learning and evolutionary algorithm framework in facilitating the optimization of composite material design for desired behavioral outcomes. [ABSTRACT FROM AUTHOR]

Methaheuristics (MHs) are techniques widely used for solving complex optimization problems. In recent years, the interest in combining MH and machine learning (ML) has grown. This integration can occur mainly in two ways: ML-in-MH and MH-in-ML. In the present work, we combine the techniques in both ways—ML-in-MH-in-ML, providing an approach in which ML is considered to improve the performance of an evolutionary algorithm (EA), whose solutions encode parameters of an ML model—artificial neural network (ANN). Our approach called TS in EA in ANN employs a reinforcement learning neighborhood (RLN) mutation based on Thompson sampling (TS). TS is a parameterless reinforcement learning method, used here to boost the EA performance. In the experiments, every candidate ANN solves a regression problem known as protein structure prediction deviation. We consider two protein datasets, one with 16,382 and the other with 45,730 samples. The results show that TS in EA in ANN performs significantly better than a canonical genetic algorithm (GA in ANN) and the evolutionary algorithm without reinforcement learning (EA in ANN). Analyses of the parameter's frequency are also performed comparing the approaches. Finally, comparisons with the literature show that except for one particular case in the largest dataset, TS in EA in ANN outperforms other approaches considered the state of the art for the addressed datasets. [ABSTRACT FROM AUTHOR]

The optimization of numerical functions with multiple independent variables was a significant challenge with numerous practical applications in process control systems, data fitting, and engineering designs. Although RNA genetic algorithms offer clear benefits in function optimization, including rapid convergence, they have low accuracy and can easily become trapped in local optima. To address these issues, a new heuristic algorithm was proposed, a gradient descent-based RNA genetic algorithm. Specifically, adaptive moment estimation (Adam) was employed as a mutation operator to improve the local development ability of the algorithm. Additionally, two new operators inspired by the inner-loop structure of RNA molecules were introduced: an inner-loop crossover operator and an inner-loop mutation operator. These operators enhance the global exploration ability of the algorithm in the early stages of evolution and enable it to escape from local optima. The algorithm consists of two stages: a pre-evolutionary stage that employs RNA genetic algorithms to identify individuals in the vicinity of the optimal region and a post-evolutionary stage that applies a adaptive gradient descent mutation to further enhance the solution's quality. When compared with the current advanced algorithms for solving function optimization problems, Adam RNA Genetic Algorithm (RNA-GA) produced better optimal solutions. In comparison with RNA-GA and Genetic Algorithm (GA) across 17 benchmark functions, Adam RNA-GA ranked first with the best result of an average rank of 1.58 according to the Friedman test. In the set of 29 functions of the CEC2017 suite, compared with heuristic algorithms such as African Vulture Optimization Algorithm, Dung Beetle Optimization, Whale Optimization Algorithm, and Grey Wolf Optimizer, Adam RNA-GA ranked first with the best result of an average rank of 1.724 according to the Friedman test. Our algorithm not only achieved significant improvements over RNA-GA but also performed excellently among various current advanced algorithms for solving function optimization problems, achieving high precision in function optimization. [ABSTRACT FROM AUTHOR]

Many-objective optimization presents unique challenges in balancing diversity and convergence of solutions. Traditional approaches struggle with this balance, leading to suboptimal solution distributions in the objective space especially at higher number of objectives. This necessitates the need for innovative strategies to adeptly manage these complexities. This study introduces a Many-Objective Artificial Hummingbird Algorithm (MaOAHA), an advanced evolutionary algorithm designed to overcome the limitations of existing many-objective optimization methods. The objectives are to improve convergence rates, maintain solution diversity, and achieve a uniform distribution in the objective space. MaOAHA implements information feedback mechanism (IFM), reference point-based selection and association, non-dominated sorting, and niche preservation. The IFM utilizes historical data from previous generations to inform the update process, thereby improving the algorithm's the exploration and exploitation capabilities. Reference point-based selection, along with non-dominated sorting, ensures solutions are both close to the Pareto front and evenly spread in the objective space. Niche preservation and density estimation strategies are employed to maintain diversity and prevent overcrowding. The comprehensive experimental analysis benchmarks MaOAHA against four leading algorithms viz. Many-Objective Gradient-Based Optimizer, Many-Objective Particle Swarm Optimizer, Reference Vector Guided Evolutionary Algorithm, and Nondominated Sorting Genetic Algorithm III. The DTLZ1–DTLZ7 benchmark sets with four, six, and eight objectives and five real-world problems (RWMaOP1–RWMaOP5) are considered for performance assessment of the selected algorithms. The results demonstrate that internal parameter-free MaOAHA significantly outperforms its counterparts, achieving better generational distance by up to 52.38%, inverse generational distance by up to 38.09%, spacing by up to 56%, spread by up to 71.42%, hypervolume by up to 44%, and runtime by up to 52%. These metrics affirm the MaOAHA's capability to enhance the decision-making processes through its adept balance of convergence, diversity, and uniformity. [ABSTRACT FROM AUTHOR]

Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize energy consumption or improve the occupant's comfort index. The energy management problem is a multi-objective problem where the user wants to reduce energy consumption while keeping the occupant's comfort index intact. To address the multi-objective problem this paper proposed an energy control system for a green environment called PMC (Power Management and Control). The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The combination of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is performed to make a fusion methodology to improve the occupant comfort index (OCI) and decrease energy utilization. The proposed framework gives a better OCI when compared with its counterparts, the Ant Bee Colony Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the PMC gives practically the same OCI but consumes less energy. The PMC framework additionally accomplished the ideal OCI (i-e 1) when compared with the existing model, FA–GA (i-e 0.98). The PMC model consumed less energy as compared to existing models such as the ABCKB, GAP, PSO, and AEO. The PMC model consumed a little bit more energy than the SOHP but provided a better OCI. The comparative outcomes show the capability of the PMC framework to reduce energy utilization and improve the OCI. Unlike other existing methodologies except for the AEO framework, the PMC technique is additionally confirmed through a simulation by controlling the indoor environment using actuators, such as fan, light, AC, and boiler. [ABSTRACT FROM AUTHOR]

With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how evolutionary multiobjective optimization techniques can be adapted to provide such an approach. With the established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the optimization framework, we use AlphaFold2 and ProteinMPNN confidence metrics to define the objective space, and a mutation operator composed of ESM-1v and ProteinMPNN to rank and then redesign the least favorable positions. Using the two-state design problem of the foldswitching protein RfaH as an in-depth case study, and PapD and calmodulin as examples of higher-dimensional design problems, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in RfaH native sequence recovery, compared to a direct application of ProteinMPNN. We suggest that this improvement is due to three factors: (i) the use of an informative mutation operator that accelerates the sequence space exploration, (ii) the parallel, iterative design process inherent to the genetic algorithm that improves upon the ProteinMPNN autoregressive sequence decoding scheme, and (iii) the explicit approximation of the Pareto front that leads to optimal design candidates representing diverse tradeoff conditions. We anticipate this approach to be readily adaptable to different models and broadly relevant for protein design tasks with complex specifications. Author summary: Proteins are the fundamental building blocks of life, and engineering them has broad applications in medicine and biotechnology. Computational methods that seek to model and predict the protein sequence-structure-function relationship have seen significant advancement from the recent development in deep learning. As more models become available, it remains an open question how to effectively combine them into a coherent computational design approach. One approach is to perform computational design with one model, and filter the design candidates with the others. In this work, we demonstrate how an optimization algorithm inspired by evolution can be adapted to provide an alternative framework that outperforms the post hoc filtering approach, especially for problems with multiple competing design specifications. Such a framework may enable researchers to more effectively integrate the strengths of different modeling approaches to tackle more complex design problems. [ABSTRACT FROM AUTHOR]

This paper focuses on robustly stabilizing stable and unstable fractional-order plants with one uncertain fractional-order term and interval uncertainties using fractional order $ PI^{\mu }D^{\lambda } $ PIμDλ controllers. Two necessary and sufficient conditions are provided to check the robust stability of the closed-loop control system. Moreover, the D-decomposition technique is utilized to determine the robust stability region of the system. Subsequently, evolutionary algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE), can be utilized to discover a fractional-order controller within the region of robust stability. This work introduces three primary contributions, outlined as follows: (1) Utilizing a graphical approach, a set of stabilizing controller is obtained. (2) Rather than employing just a single stabilizing fractional-order controller, a collection of controllers is provided for the control system. (3) Employing evolutionary algorithms to find an optimal fractional-order controller. Finally, four numerical examples are presented to validate the results. [ABSTRACT FROM AUTHOR]

The occurrence of fire leads to unparalleled loss of resources as well as human life and hence, fire detection systems must be trustworthy and less erroneous. Real-time assessment of fire conditions through predictive learning models could lead to easier decision-making and timely rescue operations. Reported works are often restricted to the use of singular rule-based algorithms which can hardly offer a comprehensive solution by adapting to changing dynamics of fire conditions due to their static features, mostly leading to inaccurate classification. In this research, an efficient fire prediction framework has been proposed by efficiently combining the outputs of recurrent neural networks which bear the advantages of short-term predictions with long short-term memory that takes care of long-term predictions. Weights to combine multi-objective optimization functions to minimize mean absolute error and root mean square error have been designed using non-dominated sorting genetic algorithm II (NSGA-II) which offers a lower time complexity. The benchmark dataset from the NIST website has been chosen for analysis and performance validation of the proposed classifier on experimental data pertaining to different fire scenarios. A Pareto optimal front has been obtained from the proposed algorithm which represents the optimum solutions. The performance of the proposed model has been exhaustively evaluated through different factors such as accuracy, RMSE, MAE, F-Measure, binary classification rate, negative predictive value, recall and precision which justifies its contribution. The proposed model reduced RMSE by 14.95–19.88% compared to baseline machine learning models along with an enhanced accuracy of 95.05% and reduced false positive rate which is better compared to reported works along with improvement in F-Measure. Results show that the proposed NSGA-II-based RNN LSTM model accurately predicts the occurrence of fire events with reduced false alarms while maintaining a low computational overhead. [ABSTRACT FROM AUTHOR]

This paper aims to solve the multi-objective operating planning problem in the radioactive environment. First, a more complicated radiation dose model is constructed, considering difficulty levels at each operating point. Based on this model, the multi-objective operating planning problem is converted to a variant traveling salesman problem (VTSP). Second, with respect to this issue, a novel combinatorial algorithm framework, namely hyper-parameter adaptive genetic algorithm (HPAGA), integrating bio-inspired optimization with reinforcement learning, is proposed, which allows for adaptive adjustment of the hyperparameters of GA so as to obtain optimal solutions efficiently. Third, comparative studies demonstrate the superior performance of the proposed HPAGA against classical evolutionary algorithms for various TSP instances. Additionally, a case study in the simulated radioactive environment implies the potential application of HPAGA in the future. [ABSTRACT FROM AUTHOR]

Distributed real time system has developed into an outstanding computing platform for parallel, high-efficiency applications. A real time system is a kind of planning where tasks must be completed with accurate results within a predetermined amount of time. It is well known that obtaining an optimal assignment of tasks for more than three processors is an NP-hard problem. This article examines the issue of assigning tasks to processors in heterogeneous distributed systems with a view to reduce cost and response time of the system while maximizing system reliability. The proposed method is carried out in two phases, Phase Ⅰ provides a hybrid HPSOGAK, that is an integration of particle swarm optimization (PSO), genetic algorithm (GA), and k -means technique while Phase Ⅱ is based on GA. By updating cluster centroids with PSO and GA and then using them like initial centroids for the k -means algorithm to generate the task-clusters, HPSOGAK produces 'm' clusters of 'r' tasks, and then their assignment onto the appropriate processor is done by using GA. The performance of GA has been improved in this article by introducing new crossover and mutation operators, and the functionality of traditional PSO has been enhanced by combining it with GA. Numerous examples from various research articles are employed to evaluate the efficiency of the proposed technique, and the numerical results are contrasted with well-known existing models. The proposed method enhances PIR values by 22.64%, efficiency by 6.93%, and response times by 23.8 on average. The experimental results demonstrate that the suggested method outperforms all comparable approaches, leading to the achievement of superior results. The developed mechanism is acceptable for an erratic number of tasks and processors with both types of fuzzy and crisp time. [ABSTRACT FROM AUTHOR]

This study demonstrates a novel method for using the Bonobo Optimizer (BO) to selective harmonic elimination in a cascaded H-Bridge Multilevel Inverter (CHB-MLI) running on solar power. The primary objective is to calculate, in real time, the optimal switching angles for eliminating low-order harmonics while maintaining a constant output voltage despite variations in the input voltage. To prove that the BO algorithm works, tests were done on a three-phase, seven-level CHB-MLI that compared it to other evolutionary algorithms like the genetic algorithm (GA) and particle Swarm optimization (PSO). An adaptive BO-Artificial neural network (BO-ANN) based system was developed to compute real-time switching angles and applied to a 7-level CHB-MLI. The results demonstrate that the BO algorithm is the most accurate and fastest evolutionary algorithm for calculating optimal switching angles. This study illustrates the BO algorithm's effective utilization in real-time harmonic elimination applications in CHB-MLI. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The Evolutionary algorithms created back in 1953, have gone through various phases of development over the years. It has been put to use to solve various problems in different domains including complex problems such as the infamous problem of Travelling Salesperson (TSP). OBJECTIVES: The main objective of this research is to find out the advancements in Evolutionary algorithms and to check whether it is still relevant in 2023. METHODS: To give an overview of the related concepts, subdomains, pros, and cons, the historical and recent developments are discussed and critiqued to provide insights into the results and a better conception of the trends in the domain. RESULTS: For a better perception of the development of evolutionary algorithms over the years, decade-wise trend analysis has been done for the past three decades. CONCLUSION: Scope of research in the domain is ever expanding and to name a few EAs for Data mining, Hybrid EAs are still under development. [ABSTRACT FROM AUTHOR]

Objective functions of which an analytical solution is very difficult or time-consuming are solved using stochastic optimization algorithms. Those optimization algorithms compute an approximate solution for objective functions. For a specific search space, the objective function might have one or more local optima along with the global optimum. When a comparison is made among the algorithms, one optimization algorithm could be more effective than others in finding a solution for certain objective functions. The most important factors affecting the success of optimization algorithms are the greatness of search space and the complexity of the objective function. Reaching the global optimum in huge search spaces is very difficult. In complex objective functions that have many local optima or where the differences between global optimum and local optima are very small, the probability of trapping into the local optimum is high. Existing optimization algorithms could be improved using the search space scanned more successfully to give a better performance. To achieve this aim, we present a novel algorithm, called Army-Inspired Genetic Algorithm (AIGA), which is inspired from military movement. The presented algorithm, apart from other optimization algorithms, searches global optima effectively by dividing the entire search area into territories instead of searching in one piece. Thus, the probability of getting trapped in a local optimum reduces and the probability of finding the global optimum increases. The presented algorithm was tested on well-known benchmark problems. The results shows that AIGA is more efficient algorithm in finding the global optimum than traditional algorithms. [ABSTRACT FROM AUTHOR]

Accurate prediction of energy consumption in district heating systems plays an important role in supporting effective and clean energy production and distribution in dense urban areas. Predictive models are needed for flexible and cost-effective operation of energy production and usage, e.g., using peak shaving or load shifting to compensate for heat losses in the pipeline. This helps to avoid exceedance of power plant capacity. The purpose of this study is to automate the process of building machine learning (ML) models to solve a short-term power demand prediction problem. The dataset contains a district heating network's measured hourly power consumption and ambient temperature for 415 days. In this paper, we propose a hybrid evolutionary-based algorithm, named GA-SHADE, for the simultaneous optimization of ML models and feature selection. The GA-SHADE algorithm is a hybrid algorithm consisting of a Genetic Algorithm (GA) and success-history-based parameter adaptation for differential evolution (SHADE). The results of the numerical experiments show that the proposed GA-SHADE algorithm allows the identification of simplified ML models with good prediction performance in terms of the optimized feature subset and model hyperparameters. The main contributions of the study are (1) using the proposed GA-SHADE, ML models with varying numbers of features and performance are obtained. (2) The proposed GA-SHADE algorithm self-adapts during operation and has only one control parameter. There is no fine-tuning required before execution. (3) Due to the evolutionary nature of the algorithm, it is not sensitive to the number of features and hyperparameters to be optimized in ML models. In conclusion, this study confirms that each optimized ML model uses a unique set and number of features. Out of the six ML models considered, SVR and NN are better candidates and have demonstrated the best performance across several metrics. All numerical experiments were compared against the measurements and proven by the standard statistical tests. [ABSTRACT FROM AUTHOR]

In this study, shell and heat exchangers are optimized using an integrated optimization framework. In this research, A structured Design of Experiments (DOE) comprising 16 trials was first conducted to systematically determine the essential parameters, including mass flow rates (mh, mc), temperatures (T1, t1, T2, t2), and heat transfer coefficients (€, TR, U). By identifying the first four principal components, PCA was able to determine 87.7% of the variance, thereby reducing the dimensionality of the problem. Performance-related aspects of the system are the focus of this approach. Key outcomes (€, TR, U) were predicted by 99% R-squared using the RSM models. Multiple factors, such as the mass flow rate and inlet temperature, were considered during the design process. The maximizing efficiency, thermal resistance, and utility were achieved by considering these factors. By using genetic algorithms, Pareto front solutions that meet the requirements of decision-makers can be found. The combination of the shell and tube heat exchangers produced better results than expected. Engineering and designers can gain practical insight into the mass flow rate, temperature, and key responses (€, TR, U) if they quantify improvements in these factors. Despite the importance of this study, it has several potential limitations, including specific experimental conditions and the need to validate it in other situations as well. Future research could investigate other factors that influence system performance. A holistic optimization framework can improve the design and engineering of heat exchangers in the future. As a result of the study, a foundation for innovative advancements in the field has been laid with tangible improvements. The study exceeded expectations by optimizing shell and heat exchanger systems using an integrated approach, thereby contributing significantly to the advancement of the field. [ABSTRACT FROM AUTHOR]

What constitutes a hard optimization problem to an Evolutionary Algorithm (EA)? To answer the question, the study of Fitness Landscape (FL) has emerged as one of the most successful techniques. FL measures the landscape depicted by the problem's cost function. Fitness Landscape Analysis (FLA) uses a set of metrics to try to determine the hardness of problems; FL metrics can be divided in descriptive and dynamic. Descriptive metrics measure the intrinsic problem features, examples of these measures are ruggedness, neutrality, basins of attraction, and epistasis. Dynamic metrics measure the evolvability of EA, examples of these measures are fitness distance correlation, and negative slope coefficient. This contribution presents a procedure called Performance Classification Models (PCM) which creates learnings models to predict the performance exhibited by Genetic Algorithms (GA) in the solution of optimization problems in the continuous domain. PCM classifies the performance in two classes (easy or difficult). The dataset has as predictor variables, FL features, and as target variable the performance exhibited by the GA. The problems used in experiments are benchmark optimization functions. A product of this approach, is a procedure to Recommend Population Size (RPS): given an optimization problem, RPS recommends the minimal population size to get an efficient level of performance. This work can be easily extended to use other metrics, or a different set of problems, or the use of other EA. Developing performance models for other EA, we can solve an instance of the algorithm selection problem. [ABSTRACT FROM AUTHOR]

Summary: Bug repair is time consuming and tedious, which hampers software maintenance. To alleviate the burden, automated program repair (APR) is proposed and has been fruitful in the last decade. Evolutionary repair is the seminal work of this field and proliferated a family of approaches. The performance of evolutionary repair approaches is affected by two main factors: (1) search space, which defines all possible patches, and (2) search algorithms, which navigate the space. Although recent approaches have achieved remarkable progress, the main challenges of the two factors still remain. On one hand, the different kinds of search space are very coarse for containing correct patches. On the other hand, the search process guided by genetic algorithms is inefficient in finding the correct patches in an appropriate time budget. In this paper, we propose MicroRepair, a new evolutionary repair approach to address the two challenges. Rather than finding statement‐level patches like existing genetic repair approaches, MicroRepair enlarges the search space by breaking the statements into finer‐granularity ingredients that consist of AST leaves. As the search space grows exponentially, the former search algorithms may become inefficient in navigating the larger space. We utilize the best multiobjective search algorithm selected from our empirical comparison of a set of search algorithms. At last, we find redundancies search in the existing genetic process, and we further design a history‐aware search strategy to accelerate the process. We evaluated MicroRepair on 224 bugs of real‐world from the benchmark Defects4J and compared it with several state‐of‐the‐art repair approaches. The evaluation results show that MicroRepair correctly repaired 26 bugs with a precision of 62%, which significantly outperforms the state‐of‐the‐art evolutionary APR approaches in terms of precision. Moreover, the history‐aware search boosts the repair execution speed by 4% on average. [ABSTRACT FROM AUTHOR]

Network function virtualization can increase the versatility of computing and network platforms, enabling them to provide more flexible services. In terms of a softwaredefined network and network function virtualization environment, this paper explores the joint virtual network function (VNF) deployment and traffic routing in multisource multicast for VNF service chain deployment to minimize the deployment overhead, transmission delay. Firstly, a multi-objective optimization model, which minimizes the deployment overhead and transmission delay, is established to tackle this challenge problem. Then, a highly efficient coding scheme, crossover, and mutation operators are designed. Based on these, an improved genetic algorithm in multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework (GA-MOEA/D) is proposed to solve this multi-objective problem. Finally, simulation experiments are conducted using two widely used network topologies in order to demonstrate the performance of the proposed algorithm. The simulation results demonstrate that the proposed algorithm can obtain the smaller deployment overhead, transmission delay, load degree and energy consumption 5.2%-15.61%, 11.2%-26.4%, 8.3%-17.6% and 7.8%-14.4% than that of the compared algorithm, respectively. [ABSTRACT FROM AUTHOR]

This paper addresses the parameter identification of a one-link flexible manipulator based on the experimental measurement of the inputs/outputs, the finite element model, and the application of evolutionary algorithms. A novel approach is proposed to find the values of inertia, stiffness, and damping parameters by minimizing the difference between the numerical model's outputs and the testbed's outputs, thus considering the joint position and acceleration of the link's tip. The dynamic model is initially obtained using the finite element method and the Lagrange principle. A prototype of a single one-link flexible manipulator is used in the experimental application, wherein the servomotor applies the input torque, and the outputs are the joint angle and the link's tip acceleration. Then, an optimization problem minimizes the difference between the numerical and experimental outputs to determine the set of parameters using evolutionary algorithms. A comparative analysis to obtain the identified parameters is established using genetic algorithms, particle swarm optimization, and differential evolution. The proposed identification approach permitted the determination of the dynamic parameters based on the complete dynamic model of the flexible-link manipulator, which is different from the approaches reported in the literature that identify a simplified model. This information is essential for the design of the motion and vibration control laws. [ABSTRACT FROM AUTHOR]

Scarfing is a type of flame treatment used to improve the quality of metal generated during steelmaking. It employs the principles of gas cutting to remove impurities and defects. Due to the high-temperature conditions and the need for uniform metal treatment, mechanical scarfing performed via a frame is preferred over manual hand scarfing. To achieve stable mechanical scarfing, a properly designed frame is essential. Generally, while using more material can create stable equipment, it also increases costs. Therefore, this study proposed a design method that selects an acceleration profile to minimize the shock on the frame during scarfing equipment operation while using a multi-objective genetic algorithm to minimize weight and maximize rigidity. Because modifying existing scarfing equipment based on the optimization results would incur additional costs and time, pre-optimizing through simulation before equipment fabrication is crucial. Optimization was achieved via the dimensional optimization of the existing frame equipment. As a result, the weight of each part and the deformation decreased by an average of 17.05 kg and 3.93%, respectively. [ABSTRACT FROM AUTHOR]

This work presents an improved parallel biobjective hybrid real-coded genetic algorithm (MORCGA-MOPSO-II). The approach is based on the combined use of the parallel Multi-Objective Real-Coded Genetic Algorithm (MORCGA) and the Multi-Objective Particle Swarm Optimization (MOPSO). At the same time, clustering-based selection techniques are used to form subpopulations of parent individuals. Using well-known clustering algorithms (e.g., k-Means, hierarchical clustering, c-means, and DBSCAN) in combination with the proposed clustering-based mutation (the CL-mutation) directed toward the obtained cluster centers allows for improving the quality of the Pareto fronts' approximations. The results of the MORCGA-MOPSO-II were compared with other well-known multi-objective evolutionary algorithms (e.g., SPEA2, NSGA-II, FCGA, MOSPO, etc.). Moreover, the MORCGA-MOPSO-II was integrated with the previously developed agent-based model of a goods exchange through the objective functions. As a result, the Pareto fronts have been obtained for the agent-based model of a goods exchange in different configurations of the initial distribution of agents. [ABSTRACT FROM AUTHOR]

An important and realistic class of scheduling problems is considered in this paper: the total earliness and tardiness minimization in the blocking flowshop, where there is no intermediate buffer between machines. Blocking occurs when a completed item or product remains on the machine until the next machine is available. We proposed a new hybrid evolutionary algorithm: the Genetic Iterated Greedy Algorithm (GIGA). In our innovative solution approach, a genetic algorithm presents a hybrid crossover based on the Iterated Greedy metaheuristic. The hybrid crossover considers the Hamming distance as an indicator of the diversity of the current population. In the first generations, the crossover will adopt larger values for the destruction parameter, and this value is gradually reduced throughout the search process. Our proposal is compared to four competitive metaheuristics reported for earliness and tardiness flowshop. Two performance indicators are considered: the Average Relative Percentage Deviation (ARPD) and the Success Rate (SR). Based on the statistical analysis of the computational experimentation, our GIGA outperformed all the implemented algorithms of the literature with statistical significance. Concerning the performance indicators, GIGA achieved ARPD = 0.02% and SR = 83.5%, pointing to the superiority of the proposed solution approach. [ABSTRACT FROM AUTHOR]

This paper presents the new Clustering-Based Hybrid Particle Swarm Optimization (CBHPSO) algorithm. This algorithm was designed to solve bi-objective optimization problems and it was used for finding trade-offs in a multisectoral agent-based model of trade interactions. This model includes multiple interacting agent enterprises belonging to different economic sectors. At the same time, the values of the control parameters of this stochastic multiagent system (MAS) need to be optimized. Therefore, CBHPSO has been developed and aggregated with this MAS by means of the objective functions. The main feature of the CBHPSO algorithm consists in the use of clustering techniques, such as the k-means algorithm, to form subsets of non-dominated solutions shared among the swarm particles in each cluster. The values of the performance metrics employed for CBHPSO and other well-known multi-objective evolutionary algorithms (SPEA2, NSGA-II, FCGA and BORCGA-BOPSO and MOPSO) were compared. As a conclusion, it was found that the velocities of decision variables in the particle swarm depend on specific non-dominant solutions of the clusters involved. It can be said that the main advantage of CBHPSO lies in the quality of the Pareto front approximation. Thus, it was demonstrated that the CBHPSO algorithm can be applied to search for improved characteristics of the employed MAS. [ABSTRACT FROM AUTHOR]