Genetic Local Search Algorithm Research Articles

Research on vehicle path planning of automated guided vehicle with simultaneous pickup and delivery with mixed time windows

AbstractThe authors investigate new Automated Guided Vehicle (AGV) Routing Problem with Simultaneous Pickup and Delivery with Mixed Time Windows (VRPSPDMTW) in smart workshops, a variation of the classic Vehicle Routing Problem (VRP). A mixed time window vehicle routing model was developed for simultaneous deliveries. This model reduces the cost of AGVs used and distribution cost, along with time window penalties. To address this complex challenge, a Hybrid Adaptive Genetic Algorithm using Variable Neighbourhood Search (AGA‐VNS) is proposed. This algorithm enhances the genetic algorithm's local search capabilities while preserving solution diversity, thereby improving both efficiency and quality of solutions. Comprehensive computational experiments are conducted, which include both VRPSPDTW test benchmark and real‐world smart factory instance studies. The outcomes reveal that the AGA‐VNS algorithm outperforms both professional solver software and advanced heuristic methods significantly. Moreover, the newly developed mixed time window model is more aligned with the requirements of real‐world production processes compared to the traditional time window model. Thus, this research not only presents novel insights into the domain of vehicle routing problems but also demonstrates its significant applicability and potential in the background of intelligent workshops.

Research on the Heterogeneous Autonomous Underwater Vehicle Cluster Scheduling Problem Based on Underwater Docking Chambers

The onboard energy supply of Autonomous Underwater Vehicles (AUVs) is one of the main limiting factors for their development. The existing methods of deploying and retrieving AUVs from mother ships consume a significant amount of energy during submerging and surfacing, resulting in a small percentage of actual working time. Underwater docking chambers provide support to AUVs underwater, saving their precious energy and addressing this issue. When an AUV cluster is assigned multiple tasks, scheduling the cluster becomes essential, and task allocation and path planning are among the core problems in AUV cluster scheduling research. In this paper, based on the underwater docking chamber, an Improved Genetic Local Search Algorithm with Prior Knowledge (IGLSAPK) is proposed to simultaneously solve the task allocation and path planning problems. Under constraints such as onboard energy supply, AUV quantity, and AUV type, the algorithm groups AUVs, assigns tasks, and plans paths to accomplish tasks at different locations, aiming to achieve overall efficiency. The algorithm first generates an initial population using prior knowledge to improve its search efficiency. It then combines an improved local search algorithm to efficiently solve large-scale, complex, and highly coupled problems. The algorithm has been evaluated through simulation experiments and comparative experiments, and the results demonstrate that the proposed algorithm outperforms other algorithms in terms of speed and optimality. The algorithm presented in this paper addresses the grouping, task allocation, and path planning problems in heterogeneous AUV clusters. Its practical significance lies in its ability to handle tasks executed by a heterogeneous AUV group, making it more practical compared to previous algorithms.

Smart charging strategy for electric vehicles based on marginal carbon emission factors and time-of-use price

As the penetration of electric vehicles (EVs) has grown rapidly in recent years, the stability and safety of electric grids are inevitably affected. Uncontrolled EV charging can reduce electric grid efficiency and increase greenhouse gas emissions (GHG). This occurs when EV charging aligns with peak demand, requiring the activation of fossil fuel-powered plants to meet the electricity demand. The time-of-use (TOU) price is a traditional management strategy for smoothing the demand power curve, but it does not consider carbon emissions. This study proposes a smart charging strategy based on an improved local search genetic algorithm (LS-GA) that considers both the TOU price and marginal emission factors (MEF). Over 47,000 charging events from 45 charging stations in Tianjin, a major city in North China, were used to simulate the charging pattern and demand. Four different charging strategies are considered in the study. The results indicate that the proposed smart charging strategy can reduce the cost and carbon emissions by up to 27% and 16% compared with uncontrolled charging, respectively. This study demonstrates the significant potential of using optimization methods in the EV smart charging problem.

Mixed-integer programming model and hybrid local search genetic algorithm for human–robot collaborative disassembly line balancing problem

ABSTRACT Human–robot collaborative technology maximises the advantages of the capabilities of humans and robots, and provides diverse operating scenarios for the remanufacturing industry. Accordingly, this paper proposes an innovative human–robot collaborative disassembly line balancing problem (HRC-DLBP). First, a mixed-integer programming (MIP) model is devised for the HRC-DLBP to minimise the number of workstations, smoothness index, and various costs. Second, a hybrid local search genetic algorithm (HLSGA) is developed to solve the proposed HRC-DLBP efficiently. According to the problem characteristics, a four-layer encoding and decoding strategy was constructed. The search mechanism of the local search operator was improved, and its search strategy was adjusted to suit the genetic algorithm structure better. Furthermore, the accuracy of the proposed MIP model and HLSGA is verified through two HRC-DLBP examples. Subsequently, three HRC-DLBP examples are used to prove that the HLSGA is superior to five other excellent algorithms. The case of the two-sided disassembly line problem reported in the literature is also solved using the HLSGA. The results are found to be significantly better than the reported outputs of the improved whale optimisation algorithm. Besides, HLSGA also outperforms the results reported in the literature in solving EOL state-oriented DLBP. Finally, the HLSGA is applied to a power battery disassembly problem, and several optimal allocation schemes are obtained.

A Neuro-Evolution Heuristic Using Active-Set Techniques to Solve a Novel Nonlinear Singular Prediction Differential Model

In this study, a novel design of a second kind of nonlinear Lane–Emden prediction differential singular model (NLE-PDSM) is presented. The numerical solutions of this model were investigated via a neuro-evolution computing intelligent solver using artificial neural networks (ANNs) optimized by global and local search genetic algorithms (GAs) and the active-set method (ASM), i.e., ANN-GAASM. The novel NLE-PDSM was derived from the standard LE and the PDSM along with the details of singular points, prediction terms and shape factors. The modeling strength of ANN was implemented to create a merit function based on the second kind of NLE-PDSM using the mean squared error, and optimization was performed through the GAASM. The corroboration, validation and excellence of the ANN-GAASM for three distinct problems were established through relative studies from exact solutions on the basis of stability, convergence and robustness. Furthermore, explanations through statistical investigations confirmed the worth of the proposed scheme.

Derivation of a semi-analytical method for designing tunable metamaterial absorbers

Metamaterial perfect absorbers (MPAs) are usually designed through two main procedures in combination with an optimization method: numerical simulation and circuit modeling. In this article, we propose a new semi-analytical method to design the MPAs. A double-layer micro-strip resonator absorber with a broad bandwidth is designed and analyzed carefully using full-wave numerical simulation combined with a genetic algorithm. The mechanism of the absorption is analyzed carefully, and it is shown that the broadband absorption is caused by the coupling between surface plasmon and Fabry-Perot (FP) resonances in different modes. Also, the role of the localized surface plasmon in coupling FP resonances through subwavelength slits is clarified based on the special shape of the micro-strip resonators. Using the obtained results, a new semi-analytical method is proposed for designing MPAs. This new method is a combination of a theoretical method derived based on the analyzed absorber mechanism and a local search genetic algorithm. The generalization of this method is investigated for different dielectrics, metals, resonator layer numbers, and in different objective frequency ranges. Results show that the method can be used in designing both broadband and multiband absorbers. Also, the method is used to design absorbers using square resonators. These resonators are polarization insensitive and support the same resonances as the micro-strips. The designed micro-strip and square resonator absorbers show favorable performance for both TE and TM polarization. We also showed this method and the proposed theoretical formulas can shorten the design process of the absorbers.

An Efficient Stochastic Numerical Computing Framework for the Nonlinear Higher Order Singular Models

The focus of the present study is to present a stochastic numerical computing framework based on Gudermannian neural networks (GNNs) together with the global and local search genetic algorithm (GA) and active-set approach (ASA), i.e., GNNs-GA-ASA. The designed computing framework GNNs-GA-ASA is tested for the higher order nonlinear singular differential model (HO-NSDM). Three different nonlinear singular variants based on the (HO-NSDM) have been solved by using the GNNs-GA-ASA and numerical solutions have been compared with the exact solutions to check the exactness of the designed scheme. The absolute errors have been performed to check the precision of the designed GNNs-GA-ASA scheme. Moreover, the aptitude of GNNs-GA-ASA is verified on precision, stability and convergence analysis, which are enhanced through efficiency, implication and dependability procedures with statistical data to solve the HO-NSDM.

Trusted QoS Assurance Approach for Composite Service

The characteristics of service computing environment, such as service loose coupling, resource heterogeneity, and protocol independence, propose higher demand for the trustworthiness of service computing systems. Trust provisioning for composite services has become a hot research spot worldwide. In this paper, quality of service (QoS) planning techniques are introduced into service composition-oriented QoS provisioning architecture. By QoS planning, the overall QoS requirement of the composite service is decomposed into separate QoS requirement for every constituent atom service, the QoS level of which can subsequently be satisfied through well-designed service entity selection policies. For any single service entity, its QoS level is variable when the deployment environment or the load of service node changes. To mitigate the uncertainty, we put forward QoS preprocessing algorithms to estimate the future QoS levels of service entities with their history execution data. Then, based on the modeling of composite service and QoS planning, we design three algorithms, which include the time preference algorithm, cost/availability (C/A) preference algorithm, and Euclidean distance preference algorithm, to select suitable atomic services meeting the user’s requirements. Finally, by combining genetic algorithm and local-search algorithm, we propose memetic algorithm to meet the QoS requirements of composite service. The effectiveness of the proposed methods by which the QoS requirements can be satisfied up to 90% is verified through experiments.

A hybrid genetic local and global search algorithm for solving no-wait flow shop problem with bi criteria

This paper addresses the m-machine no-wait Flow Shop Scheduling with Setup Times (NW-FSSWST). Two performance measures: total flow time and makespan are considered. The objective is to find a sequence that minimizing total flow time (sum C_{j}) and makespan (C_{j}) simultaneously. A Hybrid Genetic Local and Global Search Algorithm (HGLGSA) is proposed to solve the NW-FSSWST for two performance criteria. The hybrid genetic algorithm is constructed by insert-search and self-repair algorithm with self-repair function. The proposed HGLGSA is tested on 192 benchmark problems of NW-FSSWST in the literature. A full factorial experimental design is made for determined the best parameter sets that improve the performance of the proposed algorithm. The computational results are compared with the benchmark solutions from the literature. The experimental results demonstrate the effectiveness and efficiency of the proposed HGLGSA for solving NW-FSSWST.

An Improved Local Search Genetic Algorithm with a New Mapped Adaptive Operator Applied to Pseudo-Coloring Problem

In many situations, an expert must visually analyze an image arranged in grey levels. However, the human eye has strong difficulty in detecting details in this type of image, making it necessary to use artificial coloring techniques. The pseudo-coloring problem (PsCP) consists of assigning to a grey-level image, pre-segmented in K sub-regions, a set of K colors that are as dissimilar as possible. This problem is part of the well-known class of NP-Hard problems and, therefore, does not present an exact solution for all instances. Thus, meta-heuristics has been widely used to overcome this problem. In particular, genetic algorithm (GA) is one of those techniques that stands out in the literature and has already been used in PsCP. In this work, we present a new method that consists of an improvement of the GA specialized in solving the PsCP. In addition, we propose the addition of local search operators and rules for adapting parameters based on symmetric mapping functions to avoid common problems in this type of technique such as premature convergence and inadequate exploration in the search space. Our method is evaluated in three different case studies: the first consisting of the pseudo-colorization of real-world images on the RGB color space; the second consisting of the pseudo-colorization in RGB color space considering synthetic and abstract images in which its sub-regions are fully-connected; and the third consisting of the pseudo-colorization in the Munsell atlas color set. In all scenarios, our method is compared with other state-of-the-art techniques and presents superior results. Specifically, the use of mapped automatic adjustment operators proved to be powerful in boosting the proposed meta-heuristic to obtain more robust results in all evaluated instances of PsCP in all the considered case studies.

A Modified Shuffled Frog Leaping Algorithm for the Topology Optimization of Electromagnet Devices

The memetic algorithms which employ population information spreading mechanism have shown great potentials in solving complex three-dimensional black-box problems. In this paper, a newly developed memetic meta-heuristic optimization method, known as shuffled frog leaping algorithm (SFLA), is modified and applied to topology optimization of electromagnetic problems. Compared to the conventional SFLA, the proposed algorithm has an extra local search step, which allows it to escape from the local optimum, and hence avoid the problem of premature convergence to continue its search for more accurate results. To validate the performance of the proposed method, it was applied to solving the topology optimization of an interior permanent magnet motor. Two other EAs, namely the conventional SFLA and local-search genetic algorithm, were applied to study the same problem and their performances were compared with that of the proposed algorithm. The results indicate that the proposed algorithm has the best trade-off between the results of objective values and optimization time, and hence is more efficient in topology optimization of electromagnetic devices.

A genetic local search algorithm for the capacitated vehicle routing problem

A genetic local search algorithm for the capacitated vehicle routing problem

A Hybrid Local Search-Genetic Algorithm for Simultaneous Placement of DG Units and Shunt Capacitors in Radial Distribution Systems

Controlling active/reactive power in distribution systems has a great impact on its performance. The placement of distributed generators (DGs) and shunt capacitors (SCs) are the most popular mechanisms to improve the distribution system performance. In this line, this paper proposes an enhanced genetic algorithm (EGA) that combines the merits of genetic algorithm and local search to find the optimal placement and capacity of the simultaneous allocation of DGs/SCs in the radial systems. Incorporating local search scheme enhances the search space capability and increases the exploration rate for finding the global solution. The proposed procedure aims at minimizing both total real power losses and the total voltage deviation in order to enhance the distribution system performance. To prove the proposed algorithm ability and scalability, three standard test systems, IEEE 33 bus, 69 bus, and 119-bus test distribution networks, are considered. The simulation results show that the proposed EGA can efficiently search for the optimal solutions of the problem and outperforms the other existing algorithms in the literature. Moreover, an economic based cost analysis is provided for light, shoulder and heavy loading levels. It was proven, the proposed EGA leads to significant improvements in the technical and economic points of view.

Optimization of Linear Consecutive-k-Out-of-n Systems with Birnbaum Importance Based Ant Colony Optimization Algorithm

The linear consecutive-k-out-of-n: failure (good) (Lin/Con/k/n:F(G)) system consists of n interchangeable components that have different reliabilities. These components are arranged in a line path and different component assignments change the system reliability. The optimization of Lin/Con/k/n:F(G) system is to find an optimal component assignment to maximize the system reliability. As the number of components increases, the computation time for this problem increases considerably. In this paper, we propose a Birnbaum importance-based ant colony optimization (BIACO) algorithm to obtain quasi optimal assignments for such problems. We compare its performance using the Birnbaum importance based two-stage approach (BITA) and Birnbaum importance-based genetic local search (BIGLS) algorithm from previous researches. The experimental results show that the BIACO algorithm has a good performance in the optimization of Lin/Con/k/n:F(G) system.

Minimizing dispersion in multiple drone routing

In this paper, we address the problem of finding trajectories for multiple unmanned aerial vehicles deployed to perform a collaborative mission, requiring communication, coordination and situation awareness. Thus, we favor trajectories that are correlated in space and time, by proposing a metric to measure the dispersion between the trajectories. This dispersion metric is used as the objective function of the Minimum Dispersion Routing Problem. We propose a local search genetic algorithm as a method to solve this new routing problem, and we tested this approach using modified benchmark vehicle routing problem instances. Our computational results show that the approach is quite successful, yielding trajectories with the desired characteristics in terms of dispersion.

Dynamic maintenance grouping and routing for geographically dispersed production systems

This paper presents a dynamic grouping and routing approach for the maintenance optimization of a geographically dispersed production system (GDPS) consisting of several production sites located far apart from each other. Only one maintenance center is in charge of the preventive maintenance of the system. Maintenance grouping and routing are two interrelated processes but often investigated separately in literature. In this paper, these two processes are jointly studied and integrated in a global model considering economic and geographical dependencies at both component and site levels. The optimal maintenance grouped plan and routes are then determined by a combination of the Local Search Genetic Algorithm (LSGA) and Branch and Bound method (BAB). Moreover, several dynamic contexts impacting the current optimal maintenance grouped planning and routing, which may occur with time, are also studied and integrated in the joint optimization process. Thanks to this consideration, the proposed approach allows updating the grouped maintenance planning and routing to take into account the impacts of dynamic contexts when they occur. The uses and advantages of the proposed approach are illustrated through a numerical example of a GDPS consisting of 15 components located in five different sites.

Experimental analysis of design elements of scalarizing function-based multiobjective evolutionary algorithms

In this paper, we systematically study the influence of the main design elements of scalarizing function-based multiobjective evolutionary algorithms (MOEAs) on the performance of these algorithms. Such algorithms proved to be very successful in multiple computational experiments and practical applications. Well-known examples of this class of MOEAs are Jaszkiewicz’s multiobjecitve genetic local search and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The two algorithms share the same common structure and differ in two aspects, i.e., the selection of parents for recombination and the selection of weight vectors of scalarizing functions. Using three different multiobjective combinatorial optimization problems, i.e., the multiobjective symmetric traveling salesperson problem, the traveling salesperson problem with profits, and the multiobjective set covering problem, we show that the design element with the highest influence on the performance is the choice of a mechanism for parents selection, while the selection of weight vectors, either random or evenly distributed, has practically negligible influence if the number of evenly distributed weight vectors is sufficiently large.

A hybrid multi-objective genetic local search algorithm for the prize-collecting vehicle routing problem

This paper studies the prize-collecting vehicle routing problem (PCVRP), which is a new variant of the vehicle routing problem. In the PCVRP, besides the vehicle assignment and visiting sequencing, customer selection also must be determined because the available vehicles are insufficient to visit all the customers. Two types of PCVRP are often encountered in the real world, namely the PCVRP in which the number of vehicles is predetermined (PCVRP-P) and that in which the number of vehicles is not predetermined (PCVRP-NP). In general, multiple optimization objectives are required to be considered in both the PCVRP-P and PCVRP-NP. Unlike the commonly used weighted-sum method, a Pareto-based evolutionary algorithm featuring a genetic algorithm combined with a local search strategy (denoted as HGLS) is proposed for solving the multi-objective PCVRP-P. Subsequently, based on the proposed HGLS, a decomposition strategy is designed to solve the multi-objective PCVRP-NP problem by decomposing it into multiple PCVRP-P problems. In order to determine the number of PCVRP-P problems in the decomposition strategy, a vehicle-number-range determination approach is proposed to obtain a tight upper bound and lower bound of the number of vehicles. The superiority of the proposed algorithm is demonstrated by conducting several experiments on the benchmark problems.

The Multiple Traveling Salesman Problem with Backup Coverage

We consider a variant of the classical Multiple Traveling Salesmen Problem in which the distance between any two vehicles is never greater than a fixed distance D. This new feature allows salesmen to help each other timely if an emergency happens, with an estimated backup response time related to D. To achieve this goal, a spatial and temporal synchronization is required, and it incorporates routes interdependence difficulties to be overcomed. A Genetic Algorithm and a Local Search Genetic Algorithm that embodies a Variable Neighborhood Descent procedure are proposed to solve this problem. Computational results are reported on modified benchmark instances taken from TSPLIB in order to exhibit prospects of the proposed algorithms. Through an analysis of results, the highly effective performance of our proposed Local Search Genetic Algorithm is shown in comparison to the classical Genetic Algorithm.

Genetic local search algorithm for a new bi-objective arc routing problem with profit collection and dispersion of vehicles

We present a new bi-objective arc routing problem in which routes must be constructed in order to maximize collected profit and a non linear dispersion metric. A dispersion metric calculated based on instantaneous positions, suitable to capture routing characteristics found when vehicles have to travel in hostile environments, is a novelty in the routing literature. The inherent combinatorial nature of this problem makes it difficult to solve using exact methods. We propose a Multi-objective Genetic Local Search Algorithm to solve the problem and compare the results with those obtained by a well known multi-objective evolutionary algorithm. Computational experiments were performed on a new set of benchmark instances, and the results evidence that local search plays an important role in providing good approximation sets. The proposed method can be adapted to other multi-objective problems in which the exploitation provided by local search may improve the evolutionary procedures usually adopted.