Memetic Optimization Algorithm Research Articles

A hybrid niching memetic algorithm for multi-modal optimization of double row layout problem

Double row layout problem (DRLP) involves identifying the exact locations of machines participating in a production task on two rows. There are typically multiple layouts with approximately optimal material handling cost for a DRLP. These layouts often exhibit significantly different layout configurations. Identifying multiple global or local optimal layouts can provide layout designers with a wide range of options, which is of great significance for enhancing the maintainability, scalability, and customisability of the facility. However, most existing studies on DRLPs typically focus on designing a single optimal layout. In this paper, we study a multi-modal optimization of double row layout problem (MDRLP). A hybrid approach combing a fast niching memetic algorithm and linear programming (FNMA-LP) is proposed for MDRLP to locate multiple global or local optimal layouts with a similar quality. First, a fast niching memetic algorithm is developed to find a set of approximate optimal machine sequences. Then, LP is employed to optimise the exact locations of machines for each machine sequence. To evaluate the performance of the proposed algorithm, FNMA-LP is compared against three popular multi-modal algorithms and a state-of-the-art single-modal algorithm developed for DRLP. Experiments show that our approach outperforms competing approaches on almost all problem instances.

Deep reinforcement learning assisted memetic scheduling of drones for railway catenary deicing

Icy rainfall and snowfall in 2024 Spring Festival struck the high-speed railway catenary systems and caused serious traffic disruptions in central and eastern China. Deicing drones are an effective method in response to these freezing events due to their fast speed and high environmental tolerance. However, the large disaster-affected area and the large scale and complexity of catenary networks make deicing drone scheduling a very difficult problem. In this paper, we formulate two versions of deicing drone scheduling problem, one for single drone scheduling and the other for multiple drone scheduling. Unlike most existing vehicle/drone routing problems, our problem aims to minimize the total negative effect caused by the freezing events on train operations, which reflects the prime concern of the decision-maker and is highly complex. To efficiently solve the problem, we propose a reinforcement learning assisted memetic optimization algorithm, which integrates global mutation and a set of neighborhood search operators adaptively selected by deep reinforcement learning. Computational results on real-world problem instances demonstrate its significant performance advantages over selected popular optimization algorithms in the literature.

Progression Learning Convolution Neural Model-Based Sign Language Recognition Using Wearable Glove Devices

Communication among hard-of-hearing individuals presents challenges, and to facilitate communication, sign language is preferred. Many people in the deaf and hard-of-hearing communities struggle to understand sign language due to their lack of sign-mode knowledge. Contemporary researchers utilize glove and vision-based approaches to capture hand movement and analyze communication; most researchers use vision-based techniques to identify disabled people’s communication because the glove-based approach causes individuals to feel uncomfortable. However, the glove solution successfully identifies motion and hand dexterity, even though it only recognizes the numbers, words, and letters being communicated, failing to identify sentences. Therefore, artificial intelligence (AI) is integrated with the sign language prediction system to identify disabled people’s sentence-based communication. Here, wearable glove-related sign language information is utilized to analyze the recognition system’s efficiency. The collected inputs are processed using progression learning deep convolutional neural networks (PLD-CNNs). The technique known as progression learning processes sentences by dividing them into words, creating a training dataset. The model assists in efforts to understand sign language sentences. A memetic optimization algorithm is used to calibrate network performance, minimizing recognition optimization problems. This process maximizes convergence speed and reduces translation difficulties, enhancing the overall learning process. The created system is developed using the MATLAB (R2021b) tool, and its proficiency is evaluated using performance metrics. The experimental findings illustrate that the proposed system works by recognizing sign language movements with excellent precision, recall, accuracy, and F1 scores, rendering it a powerful tool in the detection of gestures in general and sign-based sentences in particular.

Optimal design and operation of reactive extrusion processes: Modeling, model validation, and optimization algorithm

AbstractIn this work, a methodology for the optimal design and operation of reactive extrusion processes in twin‐screw extruders is presented. As a process model, a very accurate mechanistic finite volume twin‐screw extruder model is used which is adapted to the reactive extrusion case. In the model, in contrast to other models, the pressure is described as a differential state in order to have a consistent set of equations. The model is compared to other models and advantages and disadvantages with respect to accuracy and computation time are discussed. For optimization, a memetic optimization algorithm (MA) that consists of a combination of a local optimization for the continuous operating parameters and an evolutionary algorithm (EA) for the global optimization of the discrete decision variables is employed to overcome the drawback of the longer model simulation time compared to simpler models. A reduction of optimization time by 90% for the MA compared to the EA was observed. The presented modeling and optimization methodology is validated on the widely investigated reactive extrusion process for ε‐Caprolactone. The methodology is very flexible and the underlying model has good prediction characteristics, thus it is applicable to a wide range of reactive extrusion processes of industrial interest.Highlights Simulation and optimization of reactive extrusion. Improved mechanistic twin‐screw extruder model. Novel approaches for pressure and residence time modeling. Efficient combination of local and global optimization in a memetic algorithm. Benchmarking of the optimization results and performance with literature data.

A Niching Regression Adaptive Memetic Algorithm for Multimodal Optimization of the Euclidean Traveling Salesman Problem

The traveling salesman problem (TSP) has been studied for many years. In particular, the multimodal optimization of the TSP is important for practical applications, because decision-makers can select the best candidate based on current conditions and requirements. In the Euclidean Traveling Salesman Problem, there are n points in Rd space with Euclidean distance between any two points, that is, d(x,y)=||x-y||2. The goal is to find a tour of minimum length visiting each point. In this paper, we only focus on the case that d=2. Recently, in order to efficiently handle the multimodal optimization of the TSP, some methods have been developed to deal with it. Nevertheless, these methods usually perform poorly for large-scale cases. In this paper, we propose a niching regression adaptive memetic algorithm to handle the multimodal optimization of the Euclidean TSP. We use the memetic algorithm as the baseline algorithm and incorporate the neighborhood strategy to maintain the population diversity. In addition, we design a novel regression partition initialization and adaptive parameter control to enhance our algorithm, and propose the concept of the redundant individual to improve the search efficiency. To validate performance of the proposed algorithm, we comprehensively conduct experiments on the multimodal optimization of TSP benchmark and the well-known TSPLIB library. The experimental results reveal that the proposed method outperforms other methods, especially for large-scale cases.

A Bilevel Gene-Based Multiobjective Memetic Algorithm for Passive Localization System Deployment Optimization

The passive localization system (PLS) is fundamental to many wireless applications. The deployment of the monitoring stations plays a key role in the performance of the PLSes. However, the workflow of the emerging cutting-edge PLSes is becoming more flexible in the complicated environment, which makes it hard to optimize the deployment. To fulfill the requirement of the real-world applications, we propose a multiobjective PLS deployment optimization model, including a surrogate geometric dilution of precision (S-GDOP) model and a system coverage indicator to meet the demand for the detection performance of the known and unknown targets. The proposed S-GDOP is separable and open to various performance-related factors in this article. Motivated by the various cooperation mechanisms and the empirical deployment patterns, we propose a bilevel gene-based multiobjective memetic algorithm within the decomposition framework to solve this problem. By maintaining an adaptive multicomponent gene population (MCGP) and a local pivot (LP)-based local search, the population evolves on two precise and consecutive gene levels, which effectively utilizes the problem and evolution-related heuristic information. The proposed algorithm outperforms another four popular algorithms in 83.3% bilateral comparisons and obtains more implicit deployment patterns, clearer deployment structures, and better converged Pareto fronts.

Thematic issue on advances in analysis and application of multi-objective memetic optimization algorithms

Thematic issue on advances in analysis and application of multi-objective memetic optimization algorithms

A Multiagent Memetic Optimization Algorithm Based on Temporal Asymptotic Surprise in Complex Networks to Reveal the Structure of the Dynamic Community.

Complex networks are used in a variety of applications. Revealing the structure of a community is one of the essential features of a network, during which remote communities are discovered in a complex network. In the real world, dynamic networks are evolving, and the problem of tracking and detecting communities at different time intervals is raised. We can use dynamic graphs to model these types of networks. This paper proposes a multiagent optimization memetic algorithm in complex networks to detect dynamic communities and calls it DYNMAMA (dynamic multiagent memetic algorithm). The temporal asymptotic surprise is used as an evaluation function of the algorithm. In the proposed algorithm, work is done on dynamic data. This algorithm does not need to specify the number of communities in advance and meets the time smoothing limit, and this applies to dynamic real-world and synthetic networks. The results of the performance of the evaluation function show that this proposed algorithm can find an optimal and more convergent solution compared to modern approaches.

A Multi-Objective Cellular Memetic Optimization Algorithm for Green Scheduling in Flexible Job Shops

In the last 30 years, a flexible job shop scheduling problem (FJSP) has been extensively explored. Production efficiency is a widely utilized objective. With the rise in environmental awareness, green objectives (e.g., energy consumption) have received a lot of attention. Nevertheless, energy consumption has received little attention. Furthermore, controllable processing times (CPT) should be considered in the field of scheduling, because they are closer to some real production. Therefore, this work investigates a FJSP with CPT (i.e., FJSP-CPT) where asymmetrical conditions and symmetrical constraints increase the difficulty of problem solving. The objectives of FJSP-CPT are to minimize simultaneously the maximum completion time (i.e., makespan) and total energy consumption (TEC). First of all, a mathematical model of this multi-objective FJSP-CPT was formulated. To optimize this problem, a novel multi-objective cellular memetic optimization algorithm (MOCMOA) was presented. The proposed MOMOA combined the advantages of cellular structure for global exploration and variable neighborhood search (VNS) for local exploitation. At last, MOCMOA was compared against other multi-objective optimization approaches by performing experiments. Numerical experiments reveal that the presented MOCMOA is superior to its competitors in 15 instances regarding three commonly used performance metrics.

A Memetic Algorithm for Solving the Robust Influence Maximization Problem on Complex Networks against Structural Failures.

Many transport systems in the real world can be modeled as networked systems. Due to limited resources, only a few nodes can be selected as seeds in the system, whose role is to spread required information or control signals as widely as possible. This problem can be modeled as the influence maximization problem. Most of the existing selection strategies are based on the invariable network structure and have not touched upon the condition that the network is under structural failures. Related studies indicate that such strategies may not completely tackle complicated diffusion tasks in reality, and the robustness of the information diffusion process against perturbances is significant. To give a numerical performance criterion of seeds under structural failure, a measure has been developed to define the robust influence maximization (RIM) problem. Further, a memetic optimization algorithm (MA) which includes several problem-orientated operators to improve the search ability, termed , has been presented to deal with the RIM problem. Experimental results on synthetic networks and real-world networks validate the effectiveness of , its superiority over existing approaches is also shown.

An Enhanced Memetic Algorithm for Feature Selection in Big Data Analytics with MapReduce

Recently, various research fields have begun dealing with massive datasets forseveral functions. The main aim of a feature selection (FS) model is to eliminate noise, repetitive, and unnecessary featuresthat reduce the efficiency of classification. In a limited period, traditional FS models cannot manage massive datasets and filterunnecessary features. It has been discovered from the state-of-the-art literature that metaheuristic algorithms perform better compared to other FS wrapper-based techniques. Common techniques such as the Genetic Algorithm (GA) andParticle Swarm Optimization (PSO) algorithm, however, suffer from slow convergence and local optima problems. Even with new generation algorithms such as Firefly heuristic and Fish Swarm Heuristic, these questions have been shown to overcome. This paper introduces an improved memetic optimization (EMO) algorithm for FS in this perspective by using conditional criteria in large datasets. The proposed EMO algorithm divides the entire dataset into sample blocksandconducts the task of learning in the map steps. The partial result obtained is combined into a final vector of feature weights in the reductionprocess which defines the appropriate collection of characteristics. Finally, the method of grouping based on the support vector machine (SVM) takes place. Within the Spark system, the proposed EMO algorithm is applied and the experimental results claim that it is superior to other approaches. The simulation results show that the maximum AUC values of 0.79 and 0.74 respectively are obtained by the EMO-FS model.

Sustainable scheduling of distributed permutation flow-shop with non-identical factory using a knowledge-based multi-objective memetic optimization algorithm

Abstract With the development of economic globalization and sustainable manufacturing, sustainable scheduling of distributed manufacturing has attracted increasing concern. However, distributed manufacturing with the non-identical factory is rarely researched. Meanwhile, scheduling can affect the sustainability of manufacturing. Thus, this paper is the first attempt to investigate a sustainable distributed permutation flow-shop scheduling problem with a non-identical factory (DPFSP-NF). We formulate a novel mathematical model of this DPFSP-NF with objectives of minimizing makespan, negative social impact (NSI), and total energy consumption (TEC). A knowledge-based multi-objective memetic optimization algorithm (KMMOA) is presented to address this DPFSP-NF. First, a new energy conservation strategy is designed and embedded in the model to reduce TEC criterion. Second, a cooperative initialization mechanism is presented to yield initial solutions with good diversity and convergence. Third, several properties of DPFSP-NF are investigated and utilized to develop the knowledge-based local search operator. The impact of parameter configuration on KMMOA is studied by the Taguchi method. Finally, we compare KMMOA to its variants and other well-known multi-objective optimization algorithms by performing a number of experiments. Experiment results demonstrate the effectiveness of each improvement component of the KMMOA, and verify that KMMOA is an effective approach to deal with the DPFSP-NF.

Community robustness and its enhancement in interdependent networks

The complex network attracts increasing attention in scientific researches and daily applications, and there has been an explosion of studies on the network theory. Concentrating on the functional information behind network structures, the community robustness has been proposed and dissected on single networks in recent studies. Currently, existing studies on the robustness of interdependent networks are limited to structural connections. As indicated by several practical dilemmas, the analyses on the invulnerability of communities and the maintenance of functional clusters in interdependent networked systems should be considered urgently. Therefore, in this paper, we first design a measure for numerically evaluating the community robustness of interdependent networks; and then guided by this measure, a memetic optimization algorithm, termed MA-CR inter, is proposed to successfully enhance the community robustness of various synthetic and real-world networks through rewiring topologies. Furthermore, two non-rewiring optimization strategies have also been modified and tested to make comparisons. The results show that these methods are valuable for enhancing networks’ community robustness as well, and different optimization strategies can be selected based on actual restraints and requirements Aiming at improving the attack tolerance of communities in interdependent networks, the obtained results facilitate the information mining from networked systems and may provide potential solutions to realistic optimization problems.

Time-optimal memetic whale optimization algorithm for hypersonic vehicle reentry trajectory optimization with no-fly zones

A novel time-optimal memetic whale optimization algorithm (WOA) integrating the Gauss pseudo-spectral methods (GPM), is proposed in this paper for the hypersonic vehicle entry trajectory optimization problem with no-fly zones. The WOA is featured with the strong global search ability and non-sensitive to the initial values, but also shows poor searching convergence speed around the global optimum. Conversely, GPM may be sensitive to the initial solution and easily trapped in a local optimum, but it also possesses more rapid convergence speed around the optimum and higher searching accuracy. Thus, a memetic optimization algorithm which contains a two-stage approach mechanism is proposed for searching the global optimum. The first searching stage, which is driven by an improved WOA (IWOA), works as an initializer of the entire searching due to its strong global search ability and non-sensitive to the initial values. The local optimum reservation and adaptive amplitude factor updating strategy are established to improve the convergent speed and the global search ability of the WOA. Once the changing of fitness value satisfies the predefined criterion, the next searching stage driven by GPM will take the place of the IWOA to expedite the search process around optimum and to obtain a precise global optimal solution. By this hybrid way, the proposed optimization algorithm may find an optimum more quickly and accurately. Simulation results show the proposed algorithm possesses faster convergence speed, higher accuracy, and stronger robustness for the hypersonic vehicle entry trajectory optimization.

A memetic algorithm for multi-objective optimization of the home health care problem

Abstract Home health care structures provide cares for the elderly, people with disabilities or patients with chronic conditions. Since the increase in demand, organizations providing home health care are eager to optimize their activities. The planning of caregivers' activities must optimize several objectives, often conflicting, that requires an extensive time to obtain a fair and valid schedule. In this paper, we address the multi-objective home health care problem with the aim of ensuring the applicability of the planning. To that end, the objectives considered in the proposed model are the minimization of the total working time of the caregivers, while maximizing the quality of service and minimizing the maximal working time difference among nurses and auxiliary nurses. A memetic algorithm for multi-objective optimization is proposed to solve the problem. Computational results on benchmark instances from the literature highlight the efficiency of the proposed algorithm in comparison with other existing metaheuristics thanks to four comparison metrics. As well, an analysis of the results exposes the trade-off between the three objectives. As a result, requiring a minimum caregivers' travel time solution leads to scarcity of the available solutions and so, cannot be demanding on the quality of the other objectives.

A new hybrid memetic multi-objective optimization algorithm for multi-objective optimization

To deal with the multi-objective optimization problems (MOPs), a meta-heuristic based on an improved shuffled frog leaping algorithm (ISFLA) which belongs to memetic evolution is presented. For the MOPs, both diversity maintenance and searching effectiveness are crucial for algorithm evolution. In this work, modified calculation of crowding distance to evaluate the density of a solution, memeplex clustering analyses based on a grid to divide the population, and new selection measure of global best individual are proposed to ensure the diversity of the algorithm. A multi-objective extremal optimization procedure (MEOP) is also introduced and incorporated into ISFLA to enable the algorithm to evolve more effectively. Finally, the experimental tests on thirteen unconstrained MOPs and DTLZ many-objective problems show that the proposed algorithm is flexible to handle MOPs and many-objective problems. The effectiveness and robustness of the proposed algorithm are also analyzed in detail.

A memetic optimization algorithm for multi-constrained multicast routing in ad hoc networks.

A mobile ad hoc network is a conventional self-configuring network where the routing optimization problem—subject to various Quality-of-Service (QoS) constraints—represents a major challenge. Unlike previously proposed solutions, in this paper, we propose a memetic algorithm (MA) employing an adaptive mutation parameter, to solve the multicast routing problem with higher search ability and computational efficiency. The proposed algorithm utilizes an updated scheme, based on statistical analysis, to estimate the best values for all MA parameters and enhance MA performance. The numerical results show that the proposed MA improved the delay and jitter of the network, while reducing computational complexity as compared to existing algorithms.

A novel dynamic reactive power planning methodology to enhance transient voltage stability

Summary Transient voltage stability (TVS) is an important issue for security operation with the development of power system topology and loads. Lots of research on reactive power planning mainly focus on static voltage stability. But the research on the dynamic reactive power planning considering TVS is very lacking so far. And most of them are only analyzed on some serious buses without covering all the weak faults. And some research considering TVS are limited under some conditions and not general enough for large-scale applications. Therefore, it is an urgent task to propose a feasible dynamic reactive power planning methodology to enhance TVS. So a novel dynamic reactive power planning methodology is proposed in this paper. In addition, although some TVS assessment indices have been proposed, they may not be suitable to be used in dynamic reactive power planning in the iteration calculation. To evaluate voltage stability after contingencies, a TVS assessment index is presented. Then an approach based on compensation sensitivity analysis is discussed for the best dynamic volt ampere reactive planning candidate locations. And a dynamic reactive power planning optimization methodology based on the memetic immune algorithm for multiobjective optimization is proposed to enhance TVS with the least static volt ampere reactive compensator investment cost and power loss cost. The methodology can cover all faults through random faults selection in the optimization. Afterwards, IEEE 39 power system and Zhejiang power grid in China are illustrated and analyzed for the improvement of TVS. It is proved that the proposed dynamic reactive power planning methodology is effective and beneficial to the security operation for power system.

A NNIA Scheme for Timetabling Problems

This paper presents a memetic multiobjective optimization algorithm based on NNIA for examination timetabling problems. In this paper, the examination timetabling problem is considered as a two-objective optimization problem while it is modeled as a single-objective optimization problem generally. Within the NNIA framework, the special crossover operator is utilized to search in the solution space; two local search techniques are employed to optimize these two objectives and a diversity-keeping strategy which consists of an elitism group operator and an extension optimization operator to ensure a sufficient number of solutions in the pareto front. The proposed algorithm was tested on the most widely used uncapacitated Carter benchmarks. Experimental results prove that the proposed algorithm is a competitive algorithm.

Memetic Simulated Annealing for Data Approximation with Local-Support Curves

This paper introduces a new memetic optimization algorithm called MeSA (Memetic Simulated Annealing) to address the data fitting problem with local-support free-form curves. The proposed method hybridizes simulated annealing with the COBYLA local search optimization method. This approach is further combined with the centripetal parameterization and the Bayesian information criterion to compute all free variables of the curve reconstruction problem with B-splines. The performance of our approach is evaluated by its application to four different shapes with local deformations and different degrees of noise and density of data points. The MeSA method has also been compared to the non-memetic version of SA. Our results show that MeSA is able to reconstruct the underlying shape of data even in the presence of noise and low density point clouds. It also outperforms SA for all the examples in this paper.