Hybrid Multi-objective Optimization Algorithm Research Articles

Enhancing the resilience of error-prone computing environments using a hybrid multi-objective optimization algorithm for edge-centric cloud computing systems

Enhancing the resilience of error-prone computing environments using a hybrid multi-objective optimization algorithm for edge-centric cloud computing systems

Multi-Objective Multi-Skill Resource-Constrained Project Scheduling Considering Flexible Resource Profiles

This paper addresses a novel multi-skill resource-constrained project scheduling problem with flexible resource profiles (F-MSRCPSP), in which the resource allocation of each activity consists of a certain number of discrete resources and is allowed to be adjusted over its duration. The F-MSRCPSP aims, therefore, to determine the flexible resource profile of each activity to minimize the make-span and total cost simultaneously. Then, a hybrid multi-objective fruit fly optimization algorithm is proposed to handle the concerned problem. In the proposed algorithm, two flexible parallel and serial schedule generation schemes are introduced, aiming to schedule activities and adjust allocated resource combinations. Additionally, two heuristic strategies are proposed to effectively select suitable resource combinations for activities. Moreover, a series of operators has been developed, including the rejoining operator, empirical re-arrangement operator, and empirical re-selection operator. These operators aim to accelerate the convergence speed and enhance the exploration of the proposed algorithm. Finally, the orthogonal test is used to select the optimal parameter combination, and comparative experiments based on tests with different scales are conducted, along with a t-test. The experimental results demonstrate that MOFOA-HS is effective in solving the F-MSRCPSP.

Crashworthiness Analysis and Optimization of Thin-Walled Structures Based on the TSSA-LSSVM Surrogate Model

Thin-Walled Structures (TWS) play a crucial role in enhancing both the collision safety and lightweight characteristics of vehicles. Structural design and optimization have demonstrated significant potential in improving their crashworthiness and overall weight reduction. Several interconnected factors, including the cross-sectional shape and parameters of distinctive structural characteristics (known as induction grooves), impact the TWS’ resistance to collisions. Another critical aspect in the optimization of TWS design is striking the delicate balance between the surrogate model’s effectiveness and precision. Taking all these factors into account, this paper conducts an in-depth investigation into the cross-sectional shape, grooves, and other geometric attributes of various TWSs through a comparative analysis. It reveals that the thin-walled structure with a Double-Ribbed Rectangle cross-section (DR-TWS) proves to be the optimal choice, particularly in low-speed axial impact scenarios. Furthermore, we present an enhanced Least Squares Support Vector Machine (LSSVM) method, augmented with an adaptive [Formula: see text]-distribution Sparrow Search Algorithm (TSSA), referred to as the TSSA-LSSVM surrogate model. The optimized design of the DR-TWS is realized through the integration of the TSSA-LSSVM surrogate model with the adaptive hybrid multi-objective particle swarm optimization (AHMOPSO) algorithm. This paper not only provides substantial theoretical support but also offers valuable guidelines for the application of TWSs in the automotive industry.

Multi-objective optimization of energy-efficient remanufacturing system scheduling problem with lot-streaming production mode

Most previous studies on the scheduling problem in remanufacturing systems have focused on single or two production stages and economic criteria such as makespan or tardiness. However, environmental criteria like energy consumption or carbon emissions, as well as the lot-streaming production mode, have not been adequately addressed. This paper presents a novel approach to the energy-efficient remanufacturing system scheduling problem with lot-streaming production mode (ERSSP-LS) for the first time, which integrates disassembly, reprocessing, and reassembly three pivotal production stages. Firstly, a multi-objective mathematical model aimed at simultaneously minimizing the total energy consumption (TEC) and makespan (Cmax) is established and presented. To further reduce the TEC, the well-accepted energy-saving measure, known as the turn off and on strategy, is also integrated. Subsequently, a hybrid multi-objective optimization algorithm called HMOFFO, which combines the fruit fly optimization algorithm and simulated annealing mechanism is developed to seek the promising Pareto solution set. Finally, several experiments on a real-life case and a group of ten random test instances are carried out and the computational results affirm the feasibility and effectiveness of the proposed HMOFFO in solving the ERSSP-LS.

Knowledge-Based Reinforcement Learning and Estimation of Distribution Algorithm for Flexible Job Shop Scheduling Problem

In this study, a flexible job shop scheduling problem with time-of-use electricity price constraint is considered. The problem includes machine processing speed, setup time, idle time, and the transportation time between machines. Both maximum completion time and total electricity price are optimized simultaneously. A hybrid multi-objective optimization algorithm of estimation of distribution algorithm and deep Q-network is proposed to solve this. The processing sequence, machine assignment, and processing speed assignment are all described using a three-dimensional solution representation. Two knowledge-based initialization strategies are designed for better performance. In the estimation of distribution algorithm component, three probability matrices corresponding to solution representation are provided. In the deep Q-network component, 34 state features are selected to describe the scheduling situation, while nine knowledge-based actions are defined to refine the scheduling solution, and the reward based on the two objectives is designed. As the knowledge for initialization and optimization strategies, five properties of the considered problem are proposed. The proposed mixed integer linear programming model of the problem is validated by exact solver CPLEX. The results of the numerical testing on wide-range scale instances show that the proposed hybrid algorithm is efficient and effective at solving the integrated flexible job shop scheduling problem.

A new hybrid multi-objective optimization algorithm for task scheduling in cloud systems

A new hybrid multi-objective optimization algorithm for task scheduling in cloud systems

Two-echelon multi-depot multi-period location-routing problem with pickup and delivery

Efficient logistics network designs can contribute to operational cost savings and efficiency improvements. In this study, a two-echelon multi-depot multi-period location-routing problem with pickup and delivery (2E-MDMPLRPPD) is proposed, seeking to construct a logistics network with high efficiency by optimizing periodic location decisions and routing schemes. On the basis of the changing time windows of customers and facilities, the proposed 2E-MDMPLRPPD explores their potential periodicity and divides the whole planning horizon into multiple periods for conducting location decisions and arranging customer services. Vehicle-sharing strategies, which encourage vehicles to be shared by facilities across multiple periods, have also been integrated into the 2E-MDMPLRPPD optimization to improve resource utilization and sustainability. The 2E-MDMPLRPPD is formulated as a bi-objective mathematical model, with the minimization of the total operational cost and number of vehicles as the two objective functions. To solve the optimization model, this study develops a hybrid multi-objective particle swarm optimization (HMOPSO) algorithm and introduces a three-dimensional (3D) k-means clustering to assist the proposed hybrid algorithm. The 3D k-means is the key to providing initial feasible candidate location strategies and simplifying the logistics network, reducing the complexity and difficulty for the following further optimization by HMOPSO. The proposed HMOPSO, integrating the Clarke–Wright saving algorithm and variable neighborhood search (VNS) into the standard MOPSO, shows good performance in solving the proposed 2E-MDMPLRPPD optimization, which is demonstrated through an algorithm comparison with MOPSO, multi-objective VNS and non-dominated sorting genetic algorithm-II. Finally, the proposed model and algorithm are applied to a real-world case study of 2E-MDMPLRPPD in Chengdu, China. A two-period location-routing strategy can achieve the best optimization results, and based on the period division and periodic location strategy, the operating cost and required vehicles can save $15,394 and 25, respectively. A series of comparative analyses in different period divisions and location selection scenarios are implemented, and the practical significance in cost-saving and efficiency improvements is verified. Thus, this study can provide insights for logistics enterprises and facilitate sustainable and efficient urban logistics operations.

Reinforcement Learning-Based Hybrid Multi-Objective Optimization Algorithm Design

The multi-objective optimization (MOO) of complex systems remains a challenging task in engineering domains. The methodological approach of applying MOO algorithms to simulation-enabled models has established itself as a standard. Despite increasing in computational power, the effectiveness and efficiency of such algorithms, i.e., their ability to identify as many Pareto-optimal solutions as possible with as few simulation samples as possible, plays a decisive role. However, the question of which class of MOO algorithms is most effective or efficient with respect to which class of problems has not yet been resolved. To tackle this performance problem, hybrid optimization algorithms that combine multiple elementary search strategies have been proposed. Despite their potential, no systematic approach for selecting and combining elementary Pareto search strategies has yet been suggested. In this paper, we propose an approach for designing hybrid MOO algorithms that uses reinforcement learning (RL) techniques to train an intelligent agent for dynamically selecting and combining elementary MOO search strategies. We present both the fundamental RL-Based Hybrid MOO (RLhybMOO) methodology and an exemplary implementation applied to mathematical test functions. The results indicate a significant performance gain of intelligent agents over elementary and static hybrid search strategies, highlighting their ability to effectively and efficiently select algorithms.

Shapley-Value-Based Hybrid Metaheuristic Multi-Objective Optimization for Energy Efficiency in an Energy-Harvesting Cognitive Radio Network

Energy efficiency and throughput are concerns for energy-harvesting cognitive radio networks. However, attaining the maximum level of both requires optimization of sensing duration, harvested energy, and transmission time. To obtain the optimal values of these multiple parameters and to maximize the average throughput and energy efficiency, a new hybrid technique for multi-objective optimization is proposed. This hybrid optimization algorithm incorporates a Shapley value and a game theoretic concept into metaheuristics. Here, particle swarm optimization grey wolf optimization (PSOGWO) is selected as the source for the advanced hybrid algorithm. The concept of the unbiased nature of wolves is also added to PSOGWO to make it more efficient. Multi-objective optimization is formulated by taking a deep look into combined spectrum sensing and energy harvesting in a cognitive radio network (CSSEH). The Pareto optimal solutions for the multi-objective optimization problem of energy efficiency and throughput can be obtained using PSOGWO by updating the velocity with the weights. In the proposed Shapley hybrid multi-objective optimization algorithm, we used Shapley values to set up the weights that, in turn, updated the velocities of the particles. This updated velocity increased the ability of particles to reach a global optimum rather than becoming trapped in local optima. The solution obtained with this hybrid algorithm is the Shapley–Pareto optimal solution. The proposed algorithm is also compared with state-of-the-art PSOGWO, unbiased PSOGWO, and GWO. The results show a significant level of improvement in terms of energy efficiency by 3.56% while reducing the sensing duration and increasing the average throughput by 21.83% in comparison with standard GWO.

Chaotic hybrid multi-objective optimization algorithm for scientific workflow scheduling in multisite clouds

A cloud is made up of many data centers, with its own set of data and resources. The reasons for employing several cloud sites to operate a workflow are that the data is already dispersed, the required resources surpass the constraints of a single site. This paper presents a hybrid multi-objective optimization algorithm denoted as HSOS-SOA, achieved by combining the Symbiotic Organisms Search and Seagull Optimization Algorithm. The HSOS-SOA uses chaotic maps to generate random numbers and performs a good trade-off between exploration and exploitation, resulting in a higher convergence rate. HSOS-SOA is used to solve scientific workflow scheduling problems in multisite cloud computing by taking into consideration elements such as makespan, cost, and reliability. A solution is chosen from the Pareto front using the knee-point approach in this approach. Extensive analyses are performed out in Microsoft Azure multisite cloud and the results exhibited that the HSOS-SOA can outperform other algorithms in terms of metrics such as IGD, Coverage Ratio, and so on. Experimental results of experiments reveal that the results in makespan improvement in the range of 5.72–28.61%, cost in the range of 5.16–45.16%, and reliability in the range of 3.11–25% over well-known metaheuristic algorithms.

Multi-objective two-stage emergent blood transshipment-allocation in COVID-19 epidemic

The problem of blood transshipment and allocation in the context of the COVID-19 epidemic has many new characteristics, such as two-stage, trans-regional, and multi-modal transportation. Considering these new characteristics, we propose a novel multi-objective optimization model for the two-stage emergent blood transshipment-allocation. The objectives considered are to optimize the quality of transshipped blood, the satisfaction of blood demand, and the overall cost including shortage penalty. An improved integer encoded hybrid multi-objective whale optimization algorithm (MOWOA) with greedy rules is then designed to solve the model. Numerical experiments demonstrate that our two-stage model is superior to one-stage optimization methods on all objectives. The degree of improvement ranges from 0.69 to 66.26%.

Kernel-based hybrid multi-objective optimization algorithm (KHMO)

A new hybrid evolutionary algorithm named Kernel-based Hybrid Multi-Objective Optimization Algorithm (KHMO) is introduced in this work. The main novelty of this algorithm is its updating rule based on the concept of a reproducing kernel in order to properly approximate the numerical gradient. In addition, a novel numerical strategy based on the computation of a normal vector to determine a searching target direction that guides the nondominanted solutions into more propitious regions is also introduced for the first time. IGD and HV metrics are used to evaluate the performance of KHMO algorithm against other competitive multi-objective methods such as MRBFO, FDEA-I, VMEF, BiasMOSaDE and GAMODE tested on ZDT and CEC 2009 problems. Moreover, the Wilcoxon test is carried out as a comparative statistical analysis in order to detect if a significant difference exists with respect to the rest of the algorithms. The corresponding numerical results showed that the introduced KHMO algorithm gains the best overall performance among all the compared algorithms in terms of diversity, coverage and convergence in most problems. Moreover, IGD and HV curves reveal a respectable convergence rate since nearly 200 iterations are enough to offer quality solutions.

Collaborative scheduling of machining-assembly in complex multiple parallel production lines environment considering kitting constraints

In multi-stage machining-assembly production, collaborative scheduling for multiple production lines can effectively improve the execution efficiency of production planning and increase the effective output of the production system. In this paper, a production scheduling mathematical model was constructed for the collaborative scheduling problem of machining-assembly multi-production lines with kitting constraints, with the optimization objectives of minimizing assembly completion time and tardiness time. For the scheduling model, the product assembly process is constrained by the machining sequence of the jobs on the machining lines. Only by collaborating on the production scheduling schemes of the machine line and the assembly line as a whole can the output efficiency of the product on the assembly line be improved. An improved hybrid multi-objective optimization algorithm named SMOEA/D is designed to solve this scheduling model. The algorithm uses adaptive parents’ selection and mutation rate strategies and integrates the Tabu search strategy for the search process in the solution space when the solution of the sub-problem has not been improved after specified search generations, to improve the local search ability and search accuracy of MOEA/D algorithm. To verify the performance of the SMOEA/D algorithm in solving machining-assembly collaborative scheduling problems in production systems with different resource configurations and scales, two sets of numerical experiments were designed, corresponding to situations where the number of operations on each production line is equal or unequal. The running results of the proposed algorithm were compared with three other well-known multi-objective algorithms. The comparison results indicate that the SMOEA/D algorithm is effective and superior for solving such problems.

A Hybrid Multi-Objective Optimization Method and Its Application to Electromagnetic Device Designs

Optimization algorithms play a critical role in electromagnetic device designs due to the ever-increasing technological and economical competition. Although evolutionary algorithm-based methods have successfully been applied to different design problems, these methods exhibit deficiencies when solving complex problems with multimodal and discontinuous objective functions, which is quite common in electromagnetic device optimization designs. In this paper, a hybrid multi-objective optimization algorithm based on a non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective particle swarm optimization method (MOPSO) is proposed. In order to enhance the convergence and diversity performance of the algorithm, a new population update mechanism of MOPSO is introduced. Moreover, an adaptive operator involving crossover and mutation is presented to achieve a better balance between global and local searches. The performance of the hybrid algorithm is validated using standard test functions and the multi-objective design of a superconducting magnetic energy storage (SMES) device. Numerical results demonstrate the effectiveness and superiority of the proposed method.

An optimization method of mine ventilation system based on R2 index hybrid multi-objective equilibrium optimization algorithm

With the increase of mining years, the mine ventilation system becomes more and more complex, causing serious energy waste. The traditional single-objective optimization model cannot​ accurately describe the complex mine ventilation system. In order to solve this problem, this paper establishes a multi-objective optimization model for mine ventilation systems considering ventilation energy consumption, fan shaft power, and fan efficiency. Furthermore, we proposed an R2 index hybrid multi-objective equilibrium optimization algorithm(R2HMEOA). Firstly, We use R2 index to improve the sorting rules of the Pareto solution. Secondly, improve the reference point strategy, and propose an elite archiving strategy based on R2 index. We use classical test functions to test the proposed algorithm. The results show that the algorithm has obvious advantages. Furthermore, we apply the algorithm to the ventilation system optimization of Wangjialing mine of Zhongmei Huajin Energy Co. Ltd. The results show that the algorithm can effectively reduce the ventilation power consumption and improve the shaft power and efficiency of the fan, which proves the practicability of the proposed algorithm.

Hybrid multi-objective optimization algorithm based on angle competition and neighborhood protection mechanism

Hybrid multi-objective optimization algorithm based on angle competition and neighborhood protection mechanism

A Botnet Detection in IoT Using a Hybrid Multi-objective Optimization Algorithm

A Botnet Detection in IoT Using a Hybrid Multi-objective Optimization Algorithm

Robust multi-response optimization considering location effect, dispersion effect, and model uncertainty using hybridization of NSGA-II and direct multi-search

This paper proposes a robust method for multi-response optimization (MRO) considering the location effect, dispersion effect, and model uncertainty simultaneously. We propose a multi-objective optimization model for MRO that simultaneously maximizes the satisfaction degrees of the local and dispersion effects. Specifically, a robust desirability function is used to model the overall satisfaction degree of multiple responses with respect to the two effects. This desirability function evaluates a solution’s performance considering the confidence intervals predicted by the regression models of location and dispersion effects, and thus, can address the problem of model uncertainty. The proposed multi-objective model yields a set of non-dominated solutions that approximate the Pareto front instead of one single solution, which provides more flexibility for decision makers to select the final best compromise solution based on their preferences. To solve the model, a hybrid multi-objective optimization algorithm called NSGAII-DMS that combines non-dominated sorting genetic algorithm II (NSGA-II) with direct multi-search (DMS) is proposed. NSGAII-DMS uses the search mechanism of NSGA-II during the early evolutionary phase to quickly find a set of non-dominated solutions and then uses the search mechanism of DMS to further tune the found non-dominated solutions. Two test examples have shown that the proposed multi-objective MRO model can produce a set of robust solutions by considering the location effect, dispersion effect, and model uncertainty. Further analyses illustrate that NSGAII-DMS shows significantly better search performance than several well-known multi-objective optimization algorithms, which include NSGA-II, SPEA2, MOEA/D, and DMS.

A novel model for sustainable waste collection arc routing problem: Pareto-based algorithms.

Municipal solid waste (MSW) management is known as one of the most crucial activities in municipalities that requires large amounts of fixed/variable and investment costs. The operational processes of collection, transportation and disposal include the major part of these costs. On the other hand, greenhouse gas (GHG) emission as environmental aspect and citizenship satisfaction as social aspect are also of particular importance, which are inevitable requirements for MSW management. This study tries to develop a novel mixed-integer linear programming (MILP) model to formulate the sustainable periodic capacitated arc routing problem (PCARP) for MSW management. The objectives are to simultaneously minimize the total cost, total environmental emission, maximize citizenship satisfaction and minimize the workload deviation. To treat the problem efficiently, a hybrid multi-objective optimization algorithm, namely, MOSA-MOIWOA is designed based on multi-objective simulated annealing algorithm (MOSA) and multi-objective invasive weed optimization algorithm (MOIWOA). To increase the algorithm performance, the Taguchi design technique is employed to set the parameters optimally. The validation of the proposed methodology is evaluated using several problem instances in the literature. Finally, the obtained results reveal the high efficiency of the suggested model and algorithm to solve the problem.

A hybrid multi-objective optimization algorithm for software requirement problem

The process of selecting software requirements aims to identify the optimal set of requirements that enhances the value of a software release while keeping costs within the budget. It is referred to as the next release problem (NRP) and is classified as a non-deterministic polynomial (NP) hard problem. Additionally, the addressed requirements are complicated by interconnections and other constraints. In the current paper, the NRP is defined as a multi-objective optimization problem with two conflicting objectives, the satisfaction of customers and cost of development, and three constraints to address two real-world instances of the NRP. A hybrid algorithm combining the multi-objective artificial bee colony and differential evolution named (HABC-DE) is proposed in this work. The proposed approach involves management from the original artificial bee colony (ABC) with operators of the differential evolution (DE) algorithm to balance the optimization process's exploitation and exploration stages. The results demonstrated that the suggested algorithm was capable of efficiently generating high-quality non-dominated solutions with 163.48 ± 4.9295 for mean and standard deviation values which can help decision-makers choose the right set of requirements for a new software release production.