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Simultaneous allocation of service times and buffer capacities in manufacturing systems in a random environment is a NP-hard combinatorial optimisation problem. This paper presents a sophisticated simulation-based optimisation approach for the design of unreliable production lines to maximise the production rate. The proposed method allows for a global search using a Genetic Algorithm (GA), which is coupled with Finite Perturbation Analysis (FPA) as a local search technique. Traditional techniques based on perturbation analysis optimise decision variables of the same nature (e.g. service time only, buffer capacity only), whereas the proposed technique simultaneously provides an allocation of service times and buffer capacities. One of the main focuses of this paper is the investigation of the persistence or absence of the buffer and service rate allocation patterns which are among the most essential insights that come from designing production lines. The results show the superiority of the combined GA-FPA approach regarding GA and FPA in terms of solution quality and convergence behaviour. Moreover, considering instances ranging from 3 to 100 machines, our numerical experiments are in line with the literature for small instances (as similar allocation patterns are identified in our work), but important differences are highlighted for medium/large instances. [ABSTRACT FROM AUTHOR]

AbstractA map becomes readable and translatable only after the use of labels. High-quality label placement (i.e., labelling) is a combinatorial optimization problem, where one or more objective functions are required. However, such objective functions are still not well achieved in existing methods with commonly used labelling rules (e.g., “avoidance of overlapping labels” and “placement at priority positions”). In this study, we define a new objective function using the maximum entropy principle and employ a modified genetic algorithm in the optimization process. Comparative experiments have been conducted with 29 existing labelling methods in a dataset of 1000 points. Experimental results show that the average percentage of non-overlapping labels by this new method reaches 94.73% (i.e., being ranked second in all 30 methods) and has no statistically significant difference from the best one (i.e., 94.87%). By further analysis, it is found that this new method not only can place more labels in priority positions than the method being ranked first but also has the potential to place labels for line and area features. Such results indicate that the maximum entropy principle may pave the way for further research in cartography. [ABSTRACT FROM AUTHOR]

Virtual machine placement (VMP) has a significant importance with respect to resource utilization in cloud data centers. Indeed, the optimized management of machine placement usually results in a significant reduction in energy consumption. VMP is a bin packing problem generalization, which is a well known hard combinatorial optimization problem. Besides being NP-hard, VMP is characterized by conflicting objectives and a noisy search space. Meta-heuristics, such as genetic algorithms, particle swarm optimization (PSO), cuckoo search (CS), tabu search and simulated annealing (SA) have been shown to be effective for this category of problems. This paper reports a performance comparison between SA, CS and PSO meta-heuristics to solve the VMP problem. In contrast to reported research work in this area, we study the performance behavior of these three meta-heuristics with respect to, not only the quality of solutions, but also the quality of the explored solution sub-space, in addition to the convergence speed towards reported solutions and the speed with which each meta-heuristic evolves towards the best reported optimized solution. Extensive simulations on randomly generated tests with sizes varying between 200 and 1000 virtual machine demands show that PSO achieves the best performance behavior with respect to all criteria. Moreover, for all tests, PSO produces a reduction of as much as 17% of the number of physical machines, 15% of the energy cost and 21% of the resource utilization of physical machines. [ABSTRACT FROM AUTHOR]

There is limited existing research that identifies the optimum rehabilitation strategy, taking utility decision-makers' risk attitudes into account. The objective of this study is to detect the critical pipes of a water distribution network (WDN) for rehabilitation, maximizing the post-earthquake serviceability of the WDN while minimizing the risk of choosing a specific rehabilitation strategy. For that purpose, a multi-objective optimization framework is formulated. System Serviceability Index (SSI) is quantified to represent the serviceability of a WDN after an earthquake. One of the two objective functions in the optimization problem maximizes the expected SSI value. The second objective function minimizes the value at risk (VaR) or conditional value at risk (CVaR) of the decision-making. The solution methodology comprises five steps: pipe seismic repair rate calculation, hydraulic modeling, and analysis, Monte Carlo simulation, nondominated sorting genetic algorithm (NSGA) for optimization, and nondominated or Pareto-optimal rehabilitation strategies identification. The proposed approach is applied to a WDN to demonstrate its effectiveness. The proposed approach offers a range of nondominated or Pareto-optimal rehabilitation strategies facilitating the decision-making based on tradeoffs between post-earthquake serviceability and risk within a specific budget limit. The proposed approach outperforms existing methods by providing risk-averse decision-makers with a set of optimal rehabilitation strategies with known risk levels. [ABSTRACT FROM AUTHOR]

Generative molecular models generate novel molecules with desired properties by searching chemical space. Traditional combinatorial optimization methods, such as genetic algorithms, have demonstrated superior performance in various molecular optimization tasks. However, these methods do not utilize docking simulation to inform the design process, and heavy dependence on the quality and quantity of available data, as well as require additional structural optimization to become candidate drugs. To address this limitation, we propose a novel model named DockingGA that combines Transformer neural networks and genetic algorithms to generate molecules with better binding affinity for specific targets. In order to generate high quality molecules, we chose the Self-referencing Chemical Structure Strings to represent the molecule and optimize the binding affinity of the molecules to different targets. Compared to other baseline models, DockingGA proves to be the optimal model in all docking results for the top 1, 10 and 100 molecules, while maintaining 100% novelty. Furthermore, the distribution of physicochemical properties demonstrates the ability of DockingGA to generate molecules with favorable and appropriate properties. This innovation creates new opportunities for the application of generative models in practical drug discovery. [ABSTRACT FROM AUTHOR]

This paper presents a novel algorithm named Five-element Cycle Integrated Mutation Optimization (FECOIMO) for solving the Traveling Thief Problem (TTP). The algorithm introduces a five-element cycle structure that integrates various mutation operations to enhance both global exploration and local exploitation capabilities. In experiments, FECOIMO was extensively tested on 39 TTP instances of varying scales and compared with five common metaheuristic algorithms: Enhanced Simulated Annealing (ESA), Improved Grey Wolf Optimization Algorithm (IGWO), Improved Whale Optimization Algorithm (IWOA), Genetic Algorithm (GA), and Profit-Guided Coordination Heuristic (PGCH). The experimental results demonstrate that FECOIMO outperforms the other algorithms across all instances, particularly excelling in large-scale instances. The results of the Friedman test show that FECOIMO significantly outperforms other algorithms in terms of average solution, maximum solution, and solution standard deviation. Additionally, although FECOIMO has a longer execution time, its complexity is comparable to that of other algorithms, and the additional computational overhead in solving complex optimization problems translates into better solutions. Therefore, FECOIMO has proven its effectiveness and robustness in handling complex combinatorial optimization problems. [ABSTRACT FROM AUTHOR]

We present machine learning (ML) methods for automatically selecting a "best" performing fast algorithm for the capacitated vehicle routing problem (CVRP) with unit demands. Algorithm selection is to automatically choose among a portfolio of algorithms the one that is predicted to work best for a given problem instance, and algorithm configuration is to automatically select algorithm's parameters that are predicted to work best for a given problem instance. We present a framework incorporating both algorithm selection and configuration for a portfolio that includes the automatically configured "Sweep Algorithm," the first generated feasible solution of the hybrid genetic search algorithm, and the Clarke and Wright algorithm. The automatically selected algorithm is shown here to deliver high‐quality feasible solutions within very small running times making it highly suitable for real‐time applications and for generating initial feasible solutions for global optimization methods for CVRP. These results bode well to the effectiveness of utilizing ML for improving combinatorial optimization methods. [ABSTRACT FROM AUTHOR]

Effective resource management constitutes a cornerstone of construction project success. This is a challenging combinatorial optimization problem with multiple and contradictory objectives whose complexity rises disproportionally with the project size and special characteristics (e.g., repetitive projects). While relevant work exists, there is still a need for thorough modeling of the practical implications of non-optimal decisions. This study proposes a multi-objective model, which can realistically represent the actual loss from not meeting the resource utilization priorities and constraints of a given project, including parameters that assess the cost of exceeding the daily resource availability, the cost of moving resources in and out of the worksite, and the cost of delaying the project completion. Optimization is performed using Genetic Algorithms, with problem setups organized in a spreadsheet format for enhanced readability and the solving is conducted via commercial software. A case study consisting of 16 repetitive projects, totaling 160 activities, tested under different objective and constraint scenarios is used to evaluate the algorithm effectiveness in different project management priorities. The main study conclusions emphasize the importance of conducting multiple analyses for effective decision-making, the increasing necessity for formal optimization as a project's size and complexity increase, and the significant support that formal optimization provides in customizing resource allocation decisions in construction projects. [ABSTRACT FROM AUTHOR]

The gas-gathering pipeline network is a critical infrastructure for collecting and conveying natural gas from the extraction site to the processing facility. This paper introduces a design optimization model for a star–tree gas-gathering pipeline network within a discrete space, aimed at determining the optimal configuration of this infrastructure. The objective is to reduce the investment required to build the network. Key decision variables include the locations of stations, the plant location, the connections between wells and stations, and the interconnections between stations. Several equality and inequality constraints are formulated, primarily addressing the affiliation between wells and stations, the transmission radius, and the capacity of the stations. The design of a star–tree pipeline network represents a complex, non-deterministic polynomial (NP) hard combinatorial optimization problem. To tackle this challenge, a hierarchical optimization framework coupled with an improved genetic algorithm (IGA) is proposed. The efficacy of the genetic algorithm is validated through testing and comparison with other traditional algorithms. Subsequently, the optimization model and solution methodology are applied to the layout design of a pipeline network. The findings reveal that the optimized network configuration reduces investment costs by 16% compared to the original design. Furthermore, when comparing the optimal layout under a star–star topology, it is observed that the investment needed for the star–star topology is 4% higher than that needed for the star–tree topology. [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]

The flexible job-shop scheduling problem (FJSP) with parallel batch processing machine (PBM) is one of those long-standing issues that needs cutting-edge approaches. It is a recent extension of standard flexible job shop scheduling problems. Despite their wide application and prevalence in practical production, it seems that current research on these types of combinatorial optimization problems remains limited and uninvestigated. More specifically, existing research mainly concentrates on the flow shop scenarios in parallel batch machines for job shop scheduling but few literature emphasis on the flexible job shop integration in these contexts. To directly address the above mentioned problems, this paper establishes an optimization model considering parallel batch processing machines, aiming to minimize the maximum completion time in operating and production environments. The proposed solution merges variable neighborhood search with multi-population genetic algorithms, conducting a neighborhood search on the elite population to reduce the likelihood of falling into local optima. Subsequently, its applicability was evaluated in computational experiments using real production scenarios from a partnering enterprise and extended datasets. The findings from the analyses indicate that the enhanced algorithm can decrease the objective value by as much as 15% compared to other standard algorithms. Importantly, the proposed approach effectively resolves flexible job shop scheduling problems involving parallel batch processing machines. The contribution of the research is providing substantial theoretical support for enterprise production scheduling. [ABSTRACT FROM AUTHOR]

Index tracking models build portfolios of limited size that replicate the performance of a market index. As the size of the index grows, it becomes impractical to find an optimal solution. As far as we know, this work proposes the first greedy randomized adaptive search procedure (GRASP) approach for index tracking. GRASP has proven to be efficient in combinatorial optimization problems and offers a solution construction procedure different from the standard index tracking optimization approaches, bringing a new perspective to the field. Results showed that the proposed GRASP approach found solutions with almost the same quality as those found by CPLEX solver in a smaller time, and the proposed local search component was competitive depending on the problem parametrization. The results found have practical implications concerning the achievement of good solutions by GRASP approaches in a smaller time when compared with hybrid genetic algorithms, and new perspectives for building GRASP heuristics for portfolio optimization problems. [ABSTRACT FROM AUTHOR]

Ultra-high-pressure (UHP) water jetting is commonly used to remove rust from ship hulls. However, few studies have been undertaken to optimize the topology of reaction rotating sprinklers (RRS) using UHP. This work proposes two contributions aimed at optimizing the RRS topology and maximizing the efficiency of rust removal. The first contribution is a novel accumulative impinging-duration model, which can accurately quantify the spiral trajectory density of multiple water jets and provide a practical means to evaluate the feasibility of RRS topology. The second contribution is the development of a new meta-heuristic algorithm called adaptive genetic algorithm (AGA), which offers high-quality solutions for the combinatorial topology optimization problem associated with RRS. The AGA converges significantly faster than the conventional genetic algorithm (CGA) and avoids being trappped at a local optimum. The effectiveness of the proposed topology optimization method is demonstrated through two case studies involving RRS with different model specifications. The AGA significantly increases the utilization ratio of water-jet energy by more than 50.6 %, outperforming CGA. The rust-removal efficiency of AGA-optimized RRS is theoretically predicted by analyzing the maximal translation speed and further validated through field experiments. Results show that the rust-removal efficiency of AGA-optimized RRS for ship hulls is greatly improved, with improvements of over 50 % and 43 % in theoretical and practical verification, respectively. This research introduces a unique quantization method and an efficient optimization algorithm for practical RRS design. [ABSTRACT FROM AUTHOR]

The global market competition is becoming increasingly fierce, and manufacturing enterprises need to invest and expand. However, the traditional financial optimization of manufacturing enterprises has faced problems such as low efficiency and inaccurate search for the optimal solution, which has made manufacturing enterprises likely to face financial risks. The investment portfolio can enable enterprises to obtain the maximum profit on a certain risk level, or reduce their investment risk on a certain return level as far as possible. If the combinatorial optimization is realized, it can be applied to the optimal selection of manufacturing enterprises' financialization. This article analyzed the respective characteristics of Genetic Algorithm (GA) and Simulated Annealing (SA) algorithms, and analyzed the combination of GA and SA algorithms to solve the optimal investment portfolio through GA-SA algorithm, thereby helping manufacturing enterprises to make the optimal choice for financialization. The experimental results of this article indicated that the GA-SA algorithm solved the problem of GA algorithm easily falling into local optima, SA algorithm's initial temperature and generation mechanism, and improved the efficiency of finding the optimal solution. Meanwhile, the experimental results showed that the average optimal solutions of Genetic Algorithm, Simulated Annealing algorithm, and GA-SA algorithms for 36 stock portfolios in Enterprise 1 were 69, 69, and 107, respectively. The average optimal solutions of the three algorithms for 36 stock portfolios in Enterprise 2 were 73, 90, and 112, respectively. This proves that the number of optimal solutions searched by GA-SA algorithm is higher than that of GA and SA algorithm, and also proves that it is effective to use GA-SA algorithm to optimize investment portfolio and help manufacturing enterprises to make optimal financial choices. [ABSTRACT FROM AUTHOR]

Symmetry is an important principle and characteristic that is prevalent in nature and artificial environments. In the three-dimensional packing problem, leveraging the inherent symmetry of goods and the symmetry of the packing space can enhance packing efficiency and utilization.The three-dimensional packing problem is an NP-hard combinatorial optimization problem in the field of modern logistics, with high computational complexity. This paper proposes an improved genetic algorithm by incorporating a fusion tabu search strategy to address this problem. The algorithm employs a three-dimensional loading mathematical model and utilizes a wall-building method under residual space constraints for stacking goods. Furthermore, adaptation of fitness variation strategy, chromosome adjustment, and tabu search algorithm are introduced to balance the algorithm's global and local search capabilities, as well as to enhance population diversity and convergence speed. Through testing on benchmark cases such as Bischoff and Ratcliff, the improved algorithm demonstrates an average increase of over 3% in packing space utilization compared to traditional genetic algorithms and other heuristic algorithms, validating its feasibility and effectiveness. The proposed improved genetic algorithm provides new insights for solving three-dimensional packing problems and optimizing logistics loading schedules, offering promising prospects for various applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid approach, fusing genetic algorithms (GA) and penguin search optimization (PSeOA), to address the flow shop scheduling problem (FSSP). GA utilizes selection, crossover, and mutation inspired by natural selection, while PSeOA emulates penguin foraging behavior for efficient exploration. The approach integrates GA's genetic diversity and solution space exploration with PSeOA's rapid convergence, further improved with FSSP-specific modifications. Extensive experiments validate its efficacy, outperforming pure GA, PSeOA, and other metaheuristics. [ABSTRACT FROM AUTHOR]

Identifying the rank of species in a complex ecosystem is a difficult task, since the rank of each species invariably depends on the interactions stipulated with other species through the adjacency matrix of the network. A common ranking method in economic and ecological networks is to sort the nodes such that the layout of the reordered adjacency matrix looks maximally nested with all nonzero entries packed in the upper left corner, called Nestedness Maximization Problem (NMP). Here we solve this problem by defining a suitable cost-energy function for the NMP which reveals the equivalence between the NMP and the Quadratic Assignment Problem, one of the most important combinatorial optimization problems, and use statistical physics techniques to derive a set of self-consistent equations whose fixed point represents the optimal nodes' rankings in an arbitrary bipartite mutualistic network. Concurrently, we present an efficient algorithm to solve the NMP that outperforms state-of-the-art network-based metrics and genetic algorithms. Eventually, our theoretical framework may be easily generalized to study the relationship between ranking and network structure beyond pairwise interactions, e.g. in higher-order networks. Species forming complex ecological or economic ecosystems are organized in hierarchies and the ranks of such species are determined by the adjacency matrix of their interaction network. We introduce a framework to calculate the ranks of species by finding the optimal permutation of rows and columns that makes the adjacency matrix maximally nested. [ABSTRACT FROM AUTHOR]

Selecting or adjusting attribute-levels (e.g. components, equipments, flavors, ingredients, prices, tastes) for multiple new and/or status quo products is an important task for a focal firm in a dynamic market. Usually, the goal is to maximize expected overall buyers' welfare based on consumers' partworths or expected revenue, market share, and profit under given assumptions. However, in general, these so-called product-line design problems cannot be solved exactly in acceptable computing time. Therefore, heuristics have been proposed: Two-stage heuristics select promising candidates for single products and evaluate sets of them as product-lines. One-stage heuristics directly search for multiple attribute-level combinations. In this paper, Ant Colony Optimization, Genetic Algorithms, Particle Swarm Optimization, Simulated Annealing and, firstly, Cluster-based Genetic Algorithm and Max-Min Ant Systems are applied to 78 small- to large-size product-line design problem instances. In contrast to former comparisons, data is generated according to a large sample of commercial conjoint analysis applications (n = 2,089). The results are promising: The firstly applied heuristics outperform the established ones. [ABSTRACT FROM AUTHOR]

Product Portfolio Planning (PPP) is one of the most critical decisions for companies to gain an edge in the competitive market. It seeks for the optimal combination of products and attribute levels offered for customers in the target market, which is a NP-hard combinatorial optimization problem. In this paper, a Probability-based Discrete Particle Swarm Optimization (PDPSO) algorithm is proposed to solve the PPP problem. In PDPSO, the particle is encoded as discrete values, which can be straightforwardly used to represent the product portfolio with discrete attributes. PDPSO adopts a probability-based mechanism to update particles. Specifically, a probability vector is used to decide the probability of three search behaviors, i.e., learning from the personal best position, global best position, or random search. In experiments, the search performance of PDPSO has been compared with that of a Genetic Algorithm (GA) and a Simulated Annealing (SA) algorithm on generated PPP problem cases with different sizes. The results indicate that PDPSO obtains significantly better optimization results than GA and SA in most cases and obtains desirable/near-optimal solutions on various PPP problem cases. A case study of notebook computer portfolio planning is also presented to illustrate the efficiency and effectiveness of PDPSO. [ABSTRACT FROM AUTHOR]

Optimizing the energy consumption of an MEC (Multi-Access Edge Computing) system is a crucial challenge for operation cost reduction and environmental conservation. In this paper, we address an MECS (MEC Server) sleep control problem that aims to reduce the energy consumption of the system while providing users with a reasonable service delay by adjusting the number of active MECSs according to the load imposed on the system. To tackle the problem, we identify two crucial issues that influence the design of an effective sleep control technique and propose methods to address each of these issues. The first issue is accurately predicting the system load. Changes in system load are spatio-temporally correlated among MECSs. By leveraging such correlation information with STGCN (Spatio-Temporal Graph Convolutional Network), we enhance the prediction accuracy of task arrival rates for each MECS. The second issue is rapidly selecting MECSs to sleep when the load distribution over an MEC system is given. The problem of choosing sleep MECS is a combinatorial optimization problem with high time complexity. To address the issue, we employ a genetic algorithm and quickly determine the optimal sleep MECS with the predicted load information for each MECS. Through simulation studies, we verify that compared to the LSTM (Long Short-Term Memory)-based method, our method increases the energy efficiency of an MEC system while providing a compatible service delay. [ABSTRACT FROM AUTHOR]

Multidirectional torsional hysteretic damper is a new type of damper that can be used to isolate and dissipate seismic effects on a structure. It can be designed to have a controllable post-elastic stiffness and exhibit high levels of damping as well as stable cyclic response. In this article, while offering a simplified numerical relationship for force-displacement response of the damper, the structure that is fitted with this innovative type of damper is optimized using the harmony search optimization procedure with discrete design variables. Numerical experiments show that the harmony search methodology can determine the damper parameters with high computational efficiency and outperform genetic algorithm and simulated annealing procedure in this regard. [ABSTRACT FROM AUTHOR]

The Minimum Broadcast Time (MBT) is a well‐known data dissemination problem whose goal is to find a broadcast scheme that minimizes the number of steps needed to execute the broadcast operation. The problem has many applications in distributed systems and, in particular, the Industry 4.0 domain. Because Industry 4.0 applications rely primarily on the use of large‐scale machine to machine communications, they need data dissemination techniques that combine high reliability with low communication latency. This work proposes a Biased Random‐Key Genetic Algorithm and a matheuristic for the MBT. We carry out experiments with our algorithms on instances commonly used in the literature (hypercube, shuffle exchange, cube‐connected cycles, de Bruijn, Harary graphs), and also on massive synthetic instances (up to 1000 vertices), allowing to cover many possibilities of real industry topologies. Our proposal is also compared with state‐of‐the‐art exact methods and heuristics. Experimental results show that our algorithm is able to outperform the best‐known heuristics for the MBT, and also that it is a very good alternative for large instances that cannot be solved by current exact methods. [ABSTRACT FROM AUTHOR]

Studying the bedrock geometry in mining and oil exploration operations to obtain its 2D pattern requires nonlinear reverse computations. Local optimization methods for solving nonlinear inverse problems are based on linearizing the changes of the model similar to a primary model and finding an objective function of minimum error from the parameters of the model; however, these optimization methods are not able to select a suitable primary function that is close enough to the general optimal value. That is to say, every objective function can have several minimum and maximum solutions. The lowest minimum is called the global minimum while the rest of them are named local minima. Therefore, in local inverse methods, the goal is to find the minimum of an objective function, and also an objective function might have a few local minima with different values. In this case, it is not suitable to use gradient-based methods for exploration purposes, unless the primary model is very close to the actual answer, which is outside the control of geological structures or the geometry of the subsurface. Despite the easy execution and high convergence rate of the local methods, there is the possibility of being trapped in local minima because these methods are dependent on the primary model, and also finding more than one optimized point in 2D or 3D simulations; this is why local optimization methods are considered deterministic algorithms. Multi-objective metaheuristic optimization algorithms are capable of searching the feasible region and they also provide a solution independent of the primary model. Searching the feasible region means finding all the feasible solutions for a problem. Each point in this region is representing a solution that can be ranked based on its value. One of the important differences between local optimization and metaheuristic methods is constraining. Constraining metaheuristic global optimization methods are only used for constraining the feasible region based on previous knowledge or estimation relations, which is different from constraining local optimization that is used for stabilizing inverse simulation. The algorithm used in the present work includes nondominated sorting genetic algorithm (NSGA-II). The NSGA-II is commonly used to solve problems with multiple, typically conflicting objective functions. This algorithm is capable of being developed and also has a high potential for solving unbounded multi-objective problems. In the present study, NSGA-II algorithm was verified and validated using the data produced by an imaginary and complex synthetic model. In the present research work, a hybrid technique of NSGA-II and TOPSIS algorithms was introduced and utilized as a viable search method for nonlinear modeling of the gravity data, and a substitute for the optimization methods. In order for a more precise examination of the performance of this algorithm, the imaginary synthetic data were used both with no noise and with up to 10% Gaussian white noise (GWN). Based on the gravimetric data of the Moghan basin and Atacama Desert, Chile, the results obtained from algorithm indicated good performance of the NSGA-II and NSGAII-TOPSIS algorithms. [ABSTRACT FROM AUTHOR]

Nowadays, multi-access edge computing (MEC) has been widely recognized as a promising technology that can support a wide range of new applications for the Internet of Things (IoT). In dynamic MEC networks, the heterogeneous computation capacities of the edge servers and the diversified requirements of the IoT applications are both asymmetric, where and when to offload and schedule the time-dependent tasks of IoT applications remains a challenge. In this paper, we propose a flexible offloading and task scheduling scheme (FLOATS) to adaptively optimize the computation of offloading decisions and scheduling priority sequences for time-dependent tasks in dynamic networks. We model the dynamic optimization problem as a multi-objective combinatorial optimization problem in an infinite time horizon, which is intractable to solve. To address this, a rolling-horizon-based optimization mechanism is designed to decompose the dynamic optimization problem into a series of static sub-problems. A genetic algorithm (GA)-based computation offloading and task scheduling algorithm is proposed for each static sub-problem. This algorithm encodes feasible solutions into two-layer chromosomes, and the optimal solution can be obtained through chromosome selection, crossover and mutation operations. The simulation results demonstrate that the proposed scheme can effectively reduce network costs in comparison to other reference schemes. [ABSTRACT FROM AUTHOR]

An automatic alpha factor mining method is proposed in this paper to assist expert traders in finding profitable alpha factors efficiently. Unlike finding qualified alpha factors by directly enumerating all possible combinations, the mining task is formulated as an iterative convolution kernel learning problem. Each kernel to be learned is associated with a unique alpha factor. To better solve the learning problem, the sparsity is introduced at the mutation step of the learning process to find simple and interpretable solutions efficiently and relieve the overfitting risks in real-world trading. A theorem is proposed to prove that the designed learning process can complete automatically in finite iterations as all convolution kernel vectors converge to zero vectors. In addition, a score function based on win rate, expected return and trade frequency is designed to evaluate the performance of market entry signals generated by the alpha factors practically. The convolution kernels with high score values are recorded and exported as the mined alpha factors. The experiment results show that the proposed method can achieve superior performance on both the China government bond dataset and the gold dataset. [ABSTRACT FROM AUTHOR]

The 0–1 Knapsack problem is a non-deterministic polynomial-time-hard combinatorial optimization problem that cannot be solved in reasonable time using traditional methods. Therefore, researchers have turned to metaheuristic algorithms for their ability to solve several combinatorial problems in a reasonable amount of time. This paper adapts the Kepler optimization algorithm using eight V-shaped and S-shaped transfer functions to create a binary variant called BKOA for solving the 0–1 Knapsack problem. Several experiments were conducted to compare the efficacy of the binary Kepler optimization algorithm to several competing optimizers when solving 20 well-known knapsack instances with dimensions ranging from 4 to 75. The experimental results demonstrate the superiority of this algorithm over other metaheuristic algorithms, except for the genetic algorithm, which is marginally superior. To further improve the binary Kepler optimization algorithm, it is combined with an enhanced improvement strategy to create a new hybrid variant. This hybrid variant, termed HBKOA, has superior exploration and exploitation capabilities that make it better than genetic algorithm and other optimizers for all performance metrics considered. The enhanced improvement strategy is also integrated with several competing optimizers, and the experimental results show that HBKOA, hybrid binary manta ray foraging optimization, and hybrid binary equilibrium optimizer are competitive for small and medium-dimensional instances and superior for the higher dimensions. [ABSTRACT FROM AUTHOR]

The job shop scheduling problem (JSSP) is a classical combinatorial optimization problem that exists widely in diverse scenarios of manufacturing systems. It is a well-known NP-hard problem, when the number of jobs increases, the difficulty of solving the problem exponentially increases. Therefore, a major challenge is to increase the solving efficiency of current algorithms. Modifying the neighborhood structure of the solutions can effectively improve the local search ability and efficiency. In this paper, a genetic Tabu search algorithm with neighborhood clipping (GTS_NC) is proposed for solving JSSP. A neighborhood solution clipping method is developed and embedded into Tabu search to improve the efficiency of the local search by clipping the search actions of unimproved neighborhood solutions. Moreover, a feasible neighborhood solution determination method is put forward, which can accurately distinguish feasible neighborhood solutions from infeasible ones. Both of the methods are based on the domain knowledge of JSSP. The proposed algorithmis compared with several competitive algorithms on benchmark instances. The experimental results show that the proposed algorithm can achieve superior results compared to other competitive algorithms. According to the numerical results of the experiments, it is verified that the neighborhood solution clipping method can accurately identify the unimproved solutions and reduces the computational time by at least 28%. [ABSTRACT FROM AUTHOR]

The gradient descent is one of the most common methods to modify the gradient parameters of the neural network, however, it may fall into the local optimum in the training process. As we all know, genetic algorithm can find the global optimal solution through global search, but it may not be efficient, always takes a lot of running time. These two methods are complementary in the running time and the cost, which inspired us to consider the combination of gradient descent and genetic algorithm. In this paper, a multi-start combinatorial optimization method based on the genetic algorithm and the gradient descent is proposed. First, the initial point is selected through the genetic algorithm. Then the combination of multi-start and gradient descent is used, which can quickly achieve the global search and improve the performance with relatively less running time and cost. Compared with the traditional genetic algorithm and gradient descent method, this proposed algorithm has a better performance in computing the global optimum efficiently. [ABSTRACT FROM AUTHOR]

In this article, a solution is proposed through a population-based metaheuristic for the Two-level Hub Location Routing Problem with Directed Tours (THLRP-DT). Hubs are facilities used to handle and dispatch resources on a given network. The goal of the THLRP-DT is to locate a set of hubs on a network and to route resources from sources to destinations, where the hubs are connected by means of an oriented cycle, and the spokes form clusters. Each cluster is composed of a unique hub, including none or some spoke nodes, connected in an oriented cycle structure. This problem appears in transportation logistics, where the flow of demands can be aggregated, resulting in economies of scale, and the orientations of arcs model a one way flow direction, which speeds up the distribution. We propose a Biased Random-Key Genetic Algorithm (BRKGA) metaheuristic, where the parameters have been calibrated using a machine learning package, which makes use of a machine learning mechanism. The results obtained using the BRKGA metaheuristic are of high quality compared to the ones found in the literature, improving solutions for instances with unknown optimal values. [ABSTRACT FROM AUTHOR]

The unit commitment problem (UCP), which includes the unit schedule and power dispatch, is a nonlinear high-dimensional and highly constrained mixed-integer combinatorial optimization problem. One challenge herein is to obtain high-quality solutions considering various constraints. Developing a competitive hybrid method is a mainstream study goal in this field, which has focused on the unit schedule optimization but less on power dispatch. Inspired by the advantage of genetic algorithms (GAs) in solving combinational optimization problems and the characteristic of grey prediction evolution algorithm (GPE) with strong exploration ability, this paper proposes a novel hybrid GA and GPE method, termed hGAGPE, to solve the UCP. In hGAGPE, GPE, as a novel real parameter stochastic search algorithm based on the grey prediction theory for data mining, is first employed to solve the power dispatch of the UCP. Meanwhile, the unit schedule is performed by the popular GA. Additionally, some heuristic repair mechanisms based on the priority list and an elite selection mechanism are incorporated to enhance the performance of hGAGPE. The proposed hGAGPE is evaluated on six test systems with generating units in the range of 10 to 100 during a 24-h scheduling period. The numerical results demonstrate the feasibility and effectiveness of hGAGPE in comparison with other existing approaches. [ABSTRACT FROM AUTHOR]

Solving problems of high dimensionality (and complexity) usually needs the intense use of technologies, like parallelism, advanced computers and new types of algorithms. MapReduce (MR) is a computing paradigm long time existing in computer science that has been proposed in the last years for dealing with big data applications, though it could also be used for many other tasks. In this article, we address big optimization: the solution to large instances of combinatorial optimization problems by using MR as the paradigm to design solvers that allow transparent runs on a varied number of computers that collaborate to find the problem solution. We study and analyze the MR technology, focusing on Hadoop, Spark, and MPI as the middleware platforms to develop genetic algorithms (GAs). From this, MRGA solvers arise using a different programming paradigm from the usual imperative transformational programming. Our objective is to confirm the expected benefits of these systems, namely file, memory, and communication management, over the resulting algorithms. We analyze our MRGA solvers from relevant points of view like scalability, speedup, and communication vs. computation time in big optimization. The results for high-dimensional datasets show that the MRGA over Hadoop outperforms the implementations in Spark and MPI frameworks. For the smallest datasets, the execution of MRGA on MPI is always faster than the executions of the remaining MRGAs. Finally, the MRGA over Spark presents the lowest communication times. Numerical and time insights are given in our work, so as to ease future comparisons of new algorithms over these three popular technologies. [ABSTRACT FROM AUTHOR]

Hybrid PV/T (Photovoltaic/thermal) systems are a robust alternative to the limitations of PV panels and thermal collectors in energy production. Improving their performance is therefore necessary. This article presents a new configuration of hybrid photovoltaic and thermal (PV/T) air/water-CuO/MgO/TiO2 collector which is optimized by seeking a better combination of design parameters which maximize the exergy performance. An energy and exergy analysis of the system is carried out and a multi-objective optimization with the genetic algorithm is developed using Matlab. These to determine the values of these nine (9) design parameters such as collector tilt angle, collector area, center to center distance between tubes, inside and outside diameter of tubes and thicknesses of the cells, of the glass layer, of the insulation, of the absorber. The other parameters are taken constant and a set of optimal solutions are sought for 1000 generations. The comparison of the different numerical results from this article with the design parameters from previous work shows good agreement. It is observed that the total exergy efficiency is maximum between the values of 23.41-36.6% and the majority of the design parameters studied in general are minimum. The discussions deduced that the minimization of the components of the bi-fluid PV/T hybrid collector could reduce the losses inside the latter by favoring the cooling as well as the displacement of the heat at the back of the PV cell. This work shows that the mixture of water and ternary nanoparticles with a flow rate of 0.00575 kg/s cooled more than air at 0.008 kg/s, but the system performed better when the two fluids operated simultaneously at 0.0035 kg/s. Due to the requirement of optimal efficiency and minimum costs, the hybridization of nanoparticles presents better thermo-economic performances. [ABSTRACT FROM AUTHOR]

This paper discusses a particular situation in project management in which an analyst attempts to prioritize several independent activities to handle all of them one by one in such a way that there would be no precedence relationships over the activities. The novelty of this research is that the structure of prioritized activities is linear in arrangement which can be considered as a combinatorial optimization problem. The paper formulates a mathematical model and applies it to two real cases in the oil and gas industry. In addition, a row generation procedure is developed to solve largescale problems and the computational results for the problem instances of size up to 300 activities are reported. The results demonstrate the applicability and efficiency of the proposed methodology. [ABSTRACT FROM AUTHOR]

Job-shop scheduling problems are among most studied problems in last years because of their importance for industries and manufacturing processes. They are classified as NP-hard problems in the strong sense. In order to tackle these problems several models and methods have been used. In this paper, we propose a hybrid metaheuristic composed of a genetic algorithm and a tabu search algorithm to solve the stochastic job-shop scheduling problem. Our contribution is based on a study of the perturbations that affect the processing times of the jobs. These perturbations, due to machine failures, occur according to a Poisson process; the results of our approach are validated on a set of instances originating from the OR-Library (Beasley, J. Oper. Res. Soc.41 (1990) 1069–1072). On the basis of these instances, the hybrid metaheuristic is used to solve the stochastic job-shop scheduling problem with the objective of minimizing the makespan as first objective and the number of critical operations as second objective during the robustness analysis. Indeed, the results show that a high value of the number of critical operations is linked to high variations of the makespan of the perturbed schedules, or in other words to a weak robustness of the relating GA's best schedule. Consequently, critical operations are not only good targets for optimizing a schedule, but also a clue of its goodness when considering stochastic and robustness aspects: the less critical operations it contains, the better it is. [ABSTRACT FROM AUTHOR]

In map generalization, displacement is the most frequently used operator to reduce the proximity conflicts caused by reducing scales or other generalization operations. Building displacement can be formalized as a combinatorial optimization problem, and a heuristic or intelligent search algorithm can be borrowed to obtain the solution. In this way, we can explicitly resolve minimum distance conflicts and control positional accuracy during the displacement. However, maintaining spatial relations and patterns of buildings can be challenging. To address spatial conflicts as well as preserve the significant spatial relations and patterns of buildings, we propose a new spatial contextual displacement algorithm based on an immune genetic algorithm. To preserve important spatial relations and global patterns of map objects and avoid topology errors, displacement safety zones are constructed by overlapping the Voronoi tessellation and buffer areas of the buildings. Additionally, a strategy to shift the buildings in a building group synchronously is used to maintain local building patterns. To demonstrate the effectiveness of our algorithm, two data sets with different building densities were tested. The results indicate that the new algorithm has obvious advantages in preventing topology errors and preserving spatial relations and patterns. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid algorithm that combines genetic algorithms (GAs) and the optimizing spotted hyena algorithm (SHOA) to solve the production shop scheduling problem. The proposed GA-SHOA algorithm incorporates genetic operators, such as uniform crossover and mutation, into the SHOA algorithm to improve its performance. We evaluated the algorithm on a set of OR library instances and compared it to other state-of-the-art optimization algorithms, including SSO, SCE-OBL, CLS-BFO and ACGA. The experimental results show that the GA-SHOA algorithm consistently finds optimal or near-optimal solutions for all tested instances, outperforming the other algorithms. Our paper contributes to the field in several ways. First, we propose a hybrid algorithm that effectively combines the exploration and exploitation capabilities of SHO and GA, resulting in a balanced and efficient search process for finding near-optimal solutions for the FSSP. Second, we tailor the SHO and GA methods to the specific requirements of the FSSP, including encoding schemes, objective function evaluation and constraint handling, which ensures that the hybrid algorithm is well suited to address the challenges posed by the FSSP. Third, we perform a comprehensive performance evaluation of the proposed hybrid algorithm, demonstrating its effectiveness in terms of solution quality and computational efficiency. Finally, we provide an in-depth analysis of the behavior of the hybrid algorithm, discussing the roles of the SHO and GA components and their interactions during the search process, which can help understand the factors contributing to the success of the algorithm and provide insight into potential improvements or adaptations to other combinatorial optimization problems. [ABSTRACT FROM AUTHOR]

Logic locking is a circuit obfuscation technique that inserts additional key gates to the original circuit in order to prevent potential threats such as circuit overproduction, piracy, and counterfeiting. The encrypted circuit generates desired outputs only when the correct keys are applied to the key gates. Previous works have identified the vulnerability of logic locking to satisfiability (SAT)-based attacks. However, SAT attacks are unscalable and have limited effectiveness on circuits with SAT-hard structures. To address the above constraints, we propose GALU, the first genetic algorithm-based logic unlocking framework that is parallelizable and significantly faster than the conventional SAT-based counterparts. GALU works by formulating circuit deobfuscation (i.e., identifying the correct keys) as a combinatorial optimization problem and approaches it using genetic algorithms (GAs). We consider key sequences as individuals in distinct populations and propose an adaptive, diversity-guided GA framework consisting of four main steps: circuit fitness evaluation, population selection, crossover, and mutation. In each iteration, the key sequences with high fitness scores are selected and transformed into the offspring key sequences. As a result of evolutionary key searching, GALU is highly scalable, effective, and efficient. To optimize the runtime overhead of logic unlocking, we integrate the design of GALU's algorithm, software and hardware in a closed loop. In particular, we identify circuit fitness evaluation as the performance bottleneck and employ hardware emulation on programmable hardware for runtime optimization. To this end, GALU framework automatically constructs customized auxiliary circuitry to pipeline the computation in constraints checking, sorting, crossover, and mutation. GALU is the first adaptive and scalable attack framework that provides the flexibility/trade-off between runtime overhead and key usability. This is achieved by producing a group of approximate keys with improving quality over time. We perform a comprehensive evaluation of GALU's performance on various benchmarks and demonstrate that GALU achieves up to 1089.2× speedup and 4268.6× more energy-efficiency compared to the state-of-the-art SAT attacks for circuit logic unlocking. [ABSTRACT FROM AUTHOR]

The artificial immune system, an excellent prototype for developing Machine Learning, is inspired by the function of the powerful natural immune system. As one of the prevalent classifiers, the Dendritic Cell Algorithm (DCA) has been widely used to solve binary problems in the real world. The classification of DCA depends on a data preprocessing procedure to generate input signals, where feature selection and signal categorization are the main work. However, the results of these studies also show that the signal generation of DCA is relatively weak, and all of them utilized a filter strategy to remove unimportant attributes. Ignoring filtered features and applying expertise may not produce an optimal classification result. To overcome these limitations, this study models feature selection and signal categorization into feature grouping problems. This study hybridizes Grouping Genetic Algorithm (GGA) with DCA to propose a novel DCA version, GGA-DCA, for accomplishing feature selection and signal categorization in a search process. The GGA-DCA aims to search for the optimal feature grouping scheme without expertise automatically. In this study, the data coding and operators of GGA are redefined for grouping tasks. The experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation. [ABSTRACT FROM AUTHOR]

The Salp Swarm Algorithm (SSA) is one of the recently proposed swarm intelligence-based algorithms, which finds its motivation in the swarming behaviour of salps when navigating and foraging in the ocean. The SSA is essentially derived to solve optimization problems that have continuous search space. SSA is a simple to implement and competitive algorithm that has been proven helpful in a variety of real-world applications. It has been explored over several optimization problems so far. In this study, an enhanced discrete version of SSA is proposed to solve the Travelling Salesman Problem (TSP). As TSP is a combinatorial optimization problem, the classical SSA is modified using swap, shift and symmetry operators for global exploration and local exploitation. Also, the 2-opt method has been incorporated with the proposed algorithm to improve the local search ability of the algorithm while addressing discrete problems. The proposed Discrete Salp Swarm Algorithm (DSSA) has evaluated over 45 TSP instances, and the computational results showed that it is a promising algorithm. To assess its performance, the proposed algorithm's results are compared with well-known algorithms such as Genetic Algorithm, Artificial Bee Colony, Spider Monkey Algorithm, Jaya Algorithm, Black Hole, Symbiotic Organism Search etc. The proposed algorithm significantly outperformed these algorithms for a majority of TSP instances. [ABSTRACT FROM AUTHOR]

Service composition is the process of combining a set of elementary or atomic services. The aim is to produce a new composite service to satisfy the user's request that cannot be satisfied by the atomic services. Combining multiple services is a complex problem that has been the subject of several research studies. The meta‐heuristic approaches are good techniques that have been used to solve several complex problems in various domains. These techniques are able to discover promising search regions and locate good quality solutions in reasonable time without exploring the whole solution space. In this paper, we deal with the problem of optimal web service composition by using meta‐heuristic approaches. Given a set of services and a set of tasks to be completed, the problem is to find the best set of services composition to complete all tasks where each service must be assigned to a given task. This problem can be modelled as a combinatorial optimization problem with a set of objective functions that need to be optimized. We search for a composite service that allows us to execute the considered tasks and offers the best quality of services (QoS). More precisely, we search for an execution plan that indicates for each task the assigned service. First, we propose a multi‐objective local search based meta‐heuristic (MO‐LS) and a multi‐objective genetic algorithm (MO‐GA) to handle our problem. Then we propose a multi‐objective memetic algorithm (MO‐MA) that combines the two methods LS and GA. The role of GA is to detect promising regions to be explored. The role of LS is to exploit efficiently the potential regions created by GA. Four objective functions are used to compute the Pareto optimal set of solutions. The main objective is to minimize cost and time and to maximize availability and reputation and produce a good composite service. The three proposed approaches namely MO‐LS, MO‐GA, and MO‐MA are evaluated on some datasets generated randomly and on the well‐known QWS dataset to select the best fit services in terms of maximum or minimum aggregated end‐to‐end QoS parameters. The numerical results are encouraging and demonstrate the effectiveness of the proposed MO‐MA for the web service composition. [ABSTRACT FROM AUTHOR]

Zhang, T.; Cai, W., and Hu, M., 2023. Configuration design of a cruise ship lifesaving system based on a genetic algorithm. Journal of Coastal Research, 39(3), 569–581. Charlotte (North Carolina), ISSN 0749-0208. To improve the configuration design of cruise ship lifesaving systems, this study proposes a system configuration design method based on a genetic algorithm. The configuration design of the lifesaving system of a cruise ship is a combinatorial optimisation problem, with various performance constraints. This study analyses the rules of the configuration design through regression analysis and establishes a mathematical model on this basis. Subsequently, aiming at the discrete problem of the selection and configuration of lifesaving equipment on cruise ships, combined with the characteristics of genetic programming, the lifesaving system required by cruise ships is analysed, classified, and described as a coding structure form of genetic programming. From the perspective of safety, a genetic programming solution strategy for equipment configuration is proposed. The results show that the system configuration design method of cruise ship lifesaving equipment proposed in this study is effective and practical, and a reasonable configuration design scheme can be obtained accurately and efficiently. [ABSTRACT FROM AUTHOR]

A graph is chordal if all its cycles of length greater than or equal to four contain a chord, i.e., an edge connecting two nonconsecutive vertices of the cycle. Given a graph G = (V, E), the chordal completion problem consists in finding the minimum set of edges to be added to G to obtain a chordal graph. It has applications in sparse linear systems, database management and computer vision programming. In this article, we developed a biased random-key genetic algorithm (BRKGA) for solving the chordal completion problem, based on the strategy of manipulating permutations that represent perfect elimination orderings of triangulations. Computational results show that the proposed heuristic improve the results of the constructive heuristics fill-in and min-degree. We also developed a strategy for injecting externally constructed feasible solutions coded as random keys into the initial population of the BRKGA that significantly improves the solutions obtained and may benefit other implementations of biased random-key genetic algorithms. [ABSTRACT FROM AUTHOR]

It is often computationally intensive to solve combinatorialoptimisation problems due to the inherent large solution space. These problems are commonly observed in the fields of engineering system design and operations. Traditional techniques are limited in handling the growing complexity and size of these problems efficiently. This paper presents a twofold update quantum-inspired genetic algorithm to solve combinatorial optimisation problems. It is generalised as an improved version of quantum-inspired evolutionary algorithm. The paper proposes a new problem formulation and the solution procedure for quantum-inspired evolutionary algorithms. An improved quantum-inspired genetic algorithm is proposed with a twofold update mechanism along with various operators. The proposed method is applied to solving a real-life engineering system optimisation problem of modular design. The results are compared using a classical genetic algorithm versus a quantum-inspired evolutionary algorithm, indicating that the proposed method outperforms the traditional methods and is more robust and more efficient. [ABSTRACT FROM AUTHOR]

This paper considers the single-machine problem with job release times and flexible preventive maintenance activities to minimize total weighted tardiness, a complicated scheduling problem for which many algorithms have been proposed in the literature. However, the considered problems are rarely solved by genetic algorithms (GAs), even though it has successfully solved various complicated combinatorial optimization problems. For the problem, we propose a trajectory-based immigration strategy, where immigrant generation is based on the given information of solution extraction knowledge matrices. We embed the immigration strategy into the GA method to improve the population's diversification process. To examine the performance of the proposed GA method, two versions of GA methods (the GA without immigration and the GA method with random immigration) and a mixed integer programming (MIP) model are also developed. Comprehensive experiments demonstrate the effectiveness of the proposed GA method by comparing the MIP model with two versions of GA methods. Overall, the proposed GA method significantly outperforms the other GA methods regarding solution quality due to the trajectory-based immigration strategy. [ABSTRACT FROM AUTHOR]

A methodology to diagnose transformer faults based on dissolved gas analysis (DGA) is proposed. Since a fault is more sensitive to the ratios of gas contents, a general ratio feature extraction framework is proposed to generate a feature subset as the input of the diagnosis model. The feature subset is evaluated by the improved case-based reasoning (CBR) and optimized by using a proposed new algorithm called the k-optimal algorithm (k-OA). The comparison results between the k-OA and the genetic algorithm (GA) show that the k-OA is more efficient in solving such a combinatorial optimization problem. The obtained optimal feature subset is used to diagnose a public transformer fault dataset, and a 92.6% diagnosis accuracy is observed as compared to that of only 85.1% diagnosis accuracy by using the original features. [ABSTRACT FROM AUTHOR]