Improved Memetic Algorithm Research Articles

Optimizing Cluster Head Selection through improved Memetic Algorithm and Blockchain Technology for Securing WSN

Optimizing Cluster Head Selection through improved Memetic Algorithm and Blockchain Technology for Securing WSN

An improved memetic algorithm for multi-objective resource-constrained flexible job shop inverse scheduling problem: An application for machining workshop

Resource-constrained flexible job shop scheduling problems are commonly encountered in some manufacturing industries, and have been widely studied in recent years. However, traditional resource constrained flexible job shop scheduling problem rarely consider the uncertainties in actual manufacturing systems, which may make the original schedule become suboptimal or even unfeasible. Therefore, a resource constrained flexible job shop inverse scheduling problem (RCFJISP) is proposed in this paper, which aims to cope with uncertain events by simultaneously adjusting the machine, worker and process parameters of the original schedule. A multi-objective optimization model is constructed to minimize the makespan, worker cost, machine energy consumption and deviation index. Furthermore, an improved memetic algorithm (IMA) is developed for solving the proposed problem. In IMA, a novel double-layer encoding mechanism is designed to enhance the capacity in exploring new solution’s domains. Three initialization strategies utilizing original scheduling information are designed to improve the quality of initial solutions. An adaptive mutation strategy and a local search mechanism are designed to enhance exploration and exploitation ability of the algorithm. And a crowding operator is proposed to reflect the diversity of the population effectively. In computational experiments, 28 extended benchmarks are constructed, and the effectiveness of the proposed strategy and algorithm is verified by comparing IMA with its 4 variants and other 4 widely used algorithms. Finally, two inverse scheduling problems of a real-world hydraulic cylinder machining workshop under two uncertain situations are studied. The results demonstrate that IMA can effectively solve the actual inverse scheduling problem. With a slight adjustment to the original scheduling, it can reduce the makespan by 11.5%, the worker cost by 8.1% and the machine energy consumption by 27.9% on average.

Opposition‐based improved memetic algorithm for placement of concurrent Internet of Things applications in fog computing

AbstractThe transpiration of a new computing standard, Fog computing exploits the computing resources immediacy to the Internet of Things (IoT) devices and hence together with the cloud servers bestow the required resources promptly. Even though the fog server provides timely services due to the fewer resources it cannot tackle the resource‐consuming applications developed by more IoT devices. Hence the placement of applications in the fog computing paradigm with more cloud and fog servers is a major challenging problem. To overcome this problem and to minimize the execution time and energy consumption of the IoT applications in a computing platform consisting of numerous IoT devices, and several fog and cloud servers, a weighted cost model is proposed. The number of parallel jobs for concurrent execution is increased due to the heterogeneity of IoT applications, and hence a pre‐scheduling technique is presented to accomplish this. In addition to that, a novel application placement method using the Opposition‐based Improved Memetic Algorithm (OBIMA) is adapted to formulate the placement of parallel IoT operations. The experimental results show that the proposed model outperforms well compared to other state‐of‐the‐art methods.

Optimal Configuration of Additional Heat Source for CHP System considering Demand Response Based on Comprehensive Benefits

An optimal configuration method for the combined heat and power (CHP) system considering demand response is proposed to scientifically and reasonably configure the parameters of the additional heat source and reduce unnecessary investment and construction costs. First, additional heat sources and demand response are utilized to decouple power generation and heating supply, enhancing the flexibility of the CHP system. Second, a multiobjective optimization configuration model of the CHP system is established, taking the system’s comprehensive satisfaction, economic cost, and wind power consumption capacity as the objectives, the unit output, and the capacity parameters of the additional heat source as the decision variables. Furthermore, an improved memetic algorithm (IMA) combined with a hierarchical sequence method is designed to solve the optimization model characterized by multiple objectives, hierarchical levels, and nonlinearity. The hierarchical sequence method solves problems sequentially based on the importance of optimization goals, ensuring the satisfaction of the configuration scheme. The IMA employs adaptive crossover and mutation probabilities, enhancing the algorithm’s convergence and quality. Finally, case analysis demonstrates that the CHP system achieves the best benefits when heat storage tanks and electric boilers are configured simultaneously. Moreover, compared to the MA, IPSO, and IABC algorithms, the IMA algorithm reduces the average economic cost by approximately 5.11%, 2.70%, and 8.43%, respectively.

Energy-efficient flexible job shop scheduling problem considering discrete operation sequence flexibility

The classical flexible job shop scheduling problem (FJSP) normally assumes that operations of each job have strict sequence constraints, i.e., each operation can be processed only after its previous operation is completed. However, in the actual production, the phenomenon that some operations of a job don't have any sequence constraints is very common. With regard to this, we firstly propose a FJSP with discrete operation sequence flexibility (FJSPDS) aiming at minimizing the makespan and total energy consumption, simultaneously. An effective mathematical model is established for the FJSPDS and its validity is proved by the CPLEX; and then an improved memetic algorithm (IMA) is designed to solve the FJSPDS. In the IMA, a new flexible sequencing method for determining process plan of each job and a right-leaning decoding method are proposed. And some effective crossover and mutation operators and an effective local search operator are designed to accelerate the convergence speed and expand the solution space of the algorithm. A total of 110 FJSPDS benchmark instances are constructed to conduct numerical simulation experiments. Experimental results show that our proposed IMA has superior performance in almost all of the instances compared with three well-known evolutionary algorithms. Our proposed model and algorithm can help the production managers who work with flexible manufacturing systems to obtain optimal scheduling schemes considering operations which have or don't have sequence constraints.

A biogeography-based optimization algorithm with modified migration operator for large-scale distributed scheduling with transportation time

To expand production scale and enhance enterprise competitiveness, modern supply chains and manufacturing systems have shifted from the single factory production to the multi-factory production network with the characteristics of large scale, diversification of varieties, redundant production factories, flexible production processes, and high-value products. This production mode is called distributed scheduling. The transportation time caused by geographically distributed factories in Distributed Scheduling Problem (DSP) with a series-parallel network plays an important role in the scheduling scheme. To minimize the global makespan over all factories, a two-segment representation has been taken as an encoding scheme while the decoding procedure applies the First Come First Served (FCFS) heuristic rule. A Biogeography-Based Optimization with Modified Migration Operator (BBO-MMO) is then proposed to address the heterogeneous DSP with transportation time between factories at adjacent processing stages, which improves the migration operator by modifying the immigration rate according to processing time and transportation time. A cosine migration model has been adopted to enhance the ability of BBO-MMO to break through local optimum. BBO-MMO is compared with the Improved Memetic Algorithm (IMA), and several groups of examples with various parameters have been generated to test the performance of BBO-MMO. The results show that the proposed BBO-MMO can effectively solve the large-scale heterogeneous DSP with transportation time.

Dynamic electric vehicle routing problem considering mid-route recharging and new demand arrival using an improved memetic algorithm

With the widespread popularity of electric vehicles (EVs) in recent years, electric vehicle routing problem (EVRP) has received considerable attention from logistics management field. However, the real-world logistics and transportation system is always time-varying, such as new customer demand arrives dynamically. This dynamic transportation environment poses a great deal of challenges upon the decision-making of EVRP since the mid-route recharging of EVs has to be considered to meet the dynamic arrival of new customer demand due to the limited battery capacity. Therefore, a dynamic EVRP (DEVRP) with mid-route recharging and new demand arrival is investigated in this study. A mathematical model considering both recharging and routing decision-makings is developed, and then an improved memetic algorithm (IMA) with adaptive local search and economical random immigrant strategy is proposed. Four peer algorithms are used to test the performance of the proposed IMA on numerous extended benchmarks. It is observed that IMA can be treated as a more effective solution tool to achieve a better routing plan as well as the improved energy efficiency of EVs.

Modified ADRC Design of Permanent Magnet Synchronous Motor Based on Improved Memetic Algorithm

In this paper, a novel modified auto disturbance rejection control (ADRC) design of a permanent magnet synchronous motor based on the improved memetic algorithm (IMA) is proposed. Firstly, there is an obvious system ripple caused by the defect that the optimal control function used in traditional ADRC cannot be differentiable and smooth at the segment point; aiming at weakening the system ripple effectively, the proposed method constructs a novel differentiable and smooth optimal control function to modify the ADRC design. Furthermore, aiming at improving the integration parameters optimization effect effectively, a novel improved memetic algorithm is proposed for obtaining the optimal parameters of ADRC. Specifically, an IMA with high-quality balance based on an adaptive nonlinear decreasing strategy for the convergence factor, Gaussian mutation mechanism, improved learning mechanism with the high-quality balance between competitive and opposition-based learning (OBL) and an elite set maintenance mechanism based on fusion distance is proposed so that these strategies can improve the optimization precision by a large margin. Finally, the experiment results of the PMSM speed control practical cases show that the ADRC based on IMA has an apparent better optimization effect than that of fuzzy PI, traditional ADRC based on the genetic algorithm and an improved ADRC based on improved moth-flame optimization.

An improved memetic algorithm to solve the energy-efficient distributed flexible job shop scheduling problem with transportation and start-stop constraints.

In the current global cooperative production environment, modern industries are confronted with intricate production plans, demanding the adoption of contemporary production scheduling strategies. Within this context, distributed manufacturing has emerged as a prominent trend. Manufacturing enterprises, especially those engaged in activities like automotive mold production and welding, are facing a significant challenge in managing a significant amount of small-scale tasks characterized by short processing times. In this situation, it becomes imperative to consider the transportation time of jobs between machines. This paper simultaneously considers the transportation time of jobs between machines and the start-stop operation of the machines, which is the first time to our knowledge. An improved memetic algorithm (IMA) is proposed to solve the multi-objective distributed flexible job shop scheduling problem (MODFJSP) with the goal of minimizing maximum completion time and energy consumption. Then, a new multi-start simulated annealing algorithm is proposed and integrated into the IMA to improve the exploration ability and diversity of the algorithm. Furthermore, a new multiple-initialization rule is designed to enhance the quality of the initial population. Additionally, four improved variable neighborhood search strategies and two energy-saving strategies are designed to enhance the search ability and reduce energy consumption. To verify the effectiveness of the IMA, we conducted extensive testing and comprehensive evaluation on 20 instances. The results indicate that, when faced with the MODFJSP, the IMA can achieve better solutions in almost all instances, which is of great significance for the improvement of production scheduling in intelligent manufacturing.

A distributed flexible job shop scheduling problem considering worker arrangement using an improved memetic algorithm

The classical distributed flexible job shop scheduling problem (DFJSP) mainly considers factory allocation, machine arrangement, job sequencing and transportation. To date, the relevant literature has not studied the DFJSPs with worker arrangement, which widely exists in practical manufacturing systems. In this paper, we investigate the DFJSP with worker arrangement (DFJSPW), where not only the factories, machines and operations, but the workers are considered simultaneously. A mixed-integer linear programming model is formulated for this problem. Correspondingly, an improved memetic algorithm (IMA) based on the structure of NSGA-II is proposed for the proposed DFJSPW whose objective is to minimize the makespan, maximum workload of machines and workload of workers simultaneously. In IMA, a simplified two-level encoding and four heuristic decoding methods are designed to encode and decode the individuals. A well-designed adaptive neighborhood search operator is developed to enhance the local search ability of IMA and speed its convergence. Fifty-eight benchmarks are constructed to evaluate the performance of our proposed IMA. Extensive experiments show that in most examples, IMA performs better than four well-known multi-objective algorithms, demonstrating the superiority of IMA in solving the DFJSPW.

An Improved Memetic Algorithm for Detecting Protein Complexes in Protein Interaction Networks.

Identifying the protein complexes in protein-protein interaction (PPI) networks is essential for understanding cellular organization and biological processes. To address the high false positive/negative rates of PPI networks and detect protein complexes with multiple topological structures, we developed a novel improved memetic algorithm (IMA). IMA first combines the topological and biological properties to obtain a weighted PPI network with reduced noise. Next, it integrates various clustering results to construct the initial populations. Furthermore, a fitness function is designed based on the five topological properties of the protein complexes. Finally, we describe the rest of our IMA method, which primarily consists of four steps: selection operator, recombination operator, local optimization strategy, and updating the population operator. In particular, IMA is a combination of genetic algorithm and a local optimization strategy, which has a strong global search ability, and searches for local optimal solutions effectively. The experimental results demonstrate that IMA performs much better than the base methods and existing state-of-the-art techniques. The source code and datasets of the IMA can be found at https://github.com/RongquanWang/IMA.

A memetic algorithm to solve uncertain energy-efficient flow shop scheduling problems

This paper addresses energy-efficient flow shop scheduling problems with uncertainties such as cancelled and new orders to minimize the total energy cost (TEC). The absenteeism of workers is also considered. As this problem is NP-hard (non-deterministic polynomial-time hard) in nature, an improved memetic algorithm (IMA) is proposed to tackle this problem. A constructive heuristic is built-in with the memetic algorithm (MA) for the improvement of the solution quality. Additionally, likelihood-based selection, crossover, and mutation operators are utilized in the IMA. A variable neighborhood search (VNS) algorithm is also hybridized as a local exploration heuristics. Broad computational experimentations are presented. The results of IMA are compared with a set of algorithms addressed in the literature. It is shown that the IMA overtakes other algorithms in terms of solution quality.

Improved Memetic Algorithm for Solving the Minimum Weight Vertex Independent Dominating Set

The minimum weight vertex independent dominating set (MWVIDS) problem is an important version of the minimum independent dominating set. The MWVIDS problem has a number of applications in many fields. However, the MWVIDS problem is known to be NP-hard and thus computationally challenging. In this work, we present the improved memetic algorithm called MSSAS for solving the MWVIDS problem. The proposed MSSAS algorithm combines probability-based dynamic optimization (PDO) (to generate good and diverse offspring solutions by assembling elements of existing good solutions) as well as a local search phase named C_LS (to seek high-quality local optima by combining the idea of constrained-based two-level configuration checking strategy and tabu mechanism). The extensive results on popular DIMACS and BHOLIB benchmarks demonstrate that MSSAS competes favorably with the state-of-the-art algorithms. In addition, we analyze the benefits of the newly raised components including two above proposed ideas with our memetic framework. It is worth mentioning that the combination of both components has excellent effects for the MWVIDS problem.

Research on Multi-Objective Hybrid Flow Shop Scheduling Problem With Dual Resource Constraints Using Improved Memetic Algorithm

Research on Multi-Objective Hybrid Flow Shop Scheduling Problem With Dual Resource Constraints Using Improved Memetic Algorithm

Improved Memetic Algorithm for Multi-depot Multi-objective Capacitated Arc Routing Problem

The capacitated arc routing problem (CARP) is a challenging vehicle routing problem with numerous real-world applications. In this paper, an extended version of CARP, the multi-depot multi-objective capacitated arc routing problem (MDMOCARP) is proposed to tackle practical requirements. Firstly, the critical edge decision mechanism and the critical edge random allocation mechanism are proposed to optimize edges between depots. Secondly, a novel adaptive probability of local search with fitness is proposed to improve the Decomposition-Based Memetic Algorithm for Multi-Objective CARP (D-MAENS). Compared with the D-MAENS algorithm, experimental results on MD-CARP instances show that the improved memetic algorithm (IMA) has performed significantly better than D-MAENS on convergence and diversity in the metric IGD and the metric HV.

Load Balanced Node Clustering scheme using Improved Memetic Algorithm based Meta-heuristic Technique for Wireless Sensor Network

In Wireless Sensor Network (WSN), the formation of the load-balanced clusters of the sensor nodes is very burning research issue. Most of the existing node clustering schemes suffer from energy-hole and non-uniform load assignment problems. These problems affect the network lifetime of the WSN severely. To solve these problems, this paper proposed an an improved Memetic algorithm-based cluster head selection scheme and also a load-balanced cluster formation scheme. The performance of the proposed scheme is compared with two well-known node clustering schemes in terms of energy consumption, residual energy, and network lifetime. Result analysis of the proposed scheme validates its superior performance over the existing node clustering schemes under different network configurations.

Improved Memetic Algorithm Enabled Intelligent Multi Agent (IMAEIMA) System for Web Mining

Wider web space, the searching of a relevant data is the most curious problem for the common people accessing the web. For retreving the relevant information the user request is given to search engine. The relevant pages combined with irrelevant pages are returned to the user. The proposed work emphasizes an Improved Memetic Algorithm Enabled Intelligent Multi Agent (IMAEIMA) for searching the most appropriate pages when submitting complex queries. Improved Memetic algorithm is the traditional genetic algorithm combined with local search and random selection. In this proposed system Improved Memetic algorithm additionally enhanced with logarithmic weight function for more accuracy. Intelligent Agents are introduced in this IMAEIMA to improve its performance and accuracy by reacting intelligently based on feedback and previous experience. This system helps to retrieve relevant pages from web databases with high precision and recall. The derived architecture reveals greater precision and recall overriding the conventional search algorithms.

Multi-objective optimal operation of hybrid AC/DC microgrid considering source-network-load coordination

Taking the consumption rate of renewable energy and the operation cost of hybrid AC/DC microgrid as the optimization objectives, the adjustment of load demand curves is carried out considering the demand side response (DSR) on the load side. The complementary utilization of renewable energy between AC area and DC area is achieved to meet the load demand on the source side. In the network side, the hybrid AC/DC microgrids purchase electricity from the power grid at the time-of-use (TOU) price and sell the surplus power of renewable energy to the power grid for profits. The improved memetic algorithm (IMA) is introduced and applied to solve the established mathematical model. The promotion effect of the proposed source-network-load coordination strategies on the optimal operation of hybrid AC/DC microgrid is verified.

An Improved Memetic Algorithm for the Partial Vertex Cover Problem

As a generalized form of the classical minimum vertex cover problem (MVCP), the minimum partial vertex cover problem not only inherits the value of MVCP in both theoretical and practical research but also extends the application scenarios under the current era of big data. In order to solve such a classical NP-hard combinatorial optimization problem, we propose an efficient memetic algorithm called MAPVC , the whose motivation behind the idea is a balance between exploitation and exploration. Specifically, the evolutionary process with a powerful backbone crossover and an adaptive mutation is adopted mainly for skipping the local optimum, and the local search with a three-layer encoding, promotive and inhibitory factor interaction is integrated into the framework to ameliorate the solutions. The experimental results on DIMACS and BHOSLIB benchmarks verify the efficiency of MAPVC through comparisons with an exact solver Gurobi and state-of-the-art local search GRASP-PVC . In addition, we also conduct the experiments on parameters setting and strategies validation to provide a comprehensive analysis of MAPVC .

Improved Memetic Algorithm and Its Application on Traveling Salesman Problem

Improved Memetic Algorithm and Its Application on Traveling Salesman Problem