Hybrid Multi-objective Algorithm Research Articles

A hybrid multi-objective algorithm based on slime mould algorithm and sine cosine algorithm for overlapping community detection in social networks

A hybrid multi-objective algorithm based on slime mould algorithm and sine cosine algorithm for overlapping community detection in social networks

A bi-level cooperating optimization for AC/DC power systems considering renewable energy integration

The integration of renewable energy on a large scale into the power system presents a significant challenge in ensuring the secure and cost-efficient operation of the system. Considering the uncertainty associated with wind and photovoltaic power, this paper develops a bi-level cooperating optimization strategy for AC-DC hybrid systems integrated with renewable energy generation. Firstly, to characterize the uncertainty of wind and photovoltaic power, the Latin hypercube sampling and the Kantorovich distance-based scenario reduction methods are employed to obtain the typical daily outputs of wind and photovoltaic power. Next, a bi-level multi-objective comprehensive optimization model is established to minimize the active power network loss and voltage deviation during system operation. To solve the bi-level optimization model, the proposed approach presents a multi-objective hybrid algorithm that combines the traditional particle swarm and whale algorithms, thereby enhancing the optimization capability of the algorithm through the incorporation of a whale enveloping search strategy into particles. Finally, the simulation results explain the effectiveness and practicability of the proposed strategy and methods, as well as the superiority of the proposed algorithm.

A data-driven game theoretic multi-objective hybrid algorithm for the Dial-A-Ride Problem with multiple time windows

The Dial-A-Ride Problem (DARP) designs pick-up and delivery routes for a set of customers. It arises in door-to-door transport services tailored to elderly and impaired people. It minimizes operational costs while accommodating as many drivers’ and customers’ constraints as possible; e.g., constraints on transit time and time windows on pick up and drop off.This paper develops a two-layer and a three-layer artificial neural network (ANN) to predict DARP parameters. The ANN is trained with real data provided by a transit agency from the Canadian city/region of Vancouver. Experimental results show that a three-layer ANN with rectified linear activation functions in conjunction with a stochastic gradient descent optimizer provides the most accurate output forecasts.Utilizing the predictions as parameter estimates for a deterministic mean DARP with multiple time windows (DARPMTW), this paper models the problem as a Company-Drivers-Customers’ theoretic game that minimizes the total route duration and maximizes the minimal satisfaction of the drivers and the customers. It shows that non-dominated Pareto feasible solutions satisfy the Nash equilibrium conditions of the game-theoretic model. It then creates a multi-objective hybrid adaptive large neighborhood search (DD-HALNS) to estimate the Pareto front. The multi-objective DD-HALNS algorithm confines its search to feasible solutions that satisfy the Nash equilibrium conditions and employs local search operators based on their success rates. The operators are controlled by a learning mechanism that utilizes previous successful moves, cost savings, and utility maximization. Four hybridization operators are applied, namely Simulated Annealing, Tabu Lists, Genetic Crossover, and Restarts. The results of experiments conducted on real-world DARPMTW instances demonstrate the ability of the multi-objective DD-HALNS to enhance the best-known routing solutions, as well as its implementability.

An Efficient Hybrid Multi-Objective Optimization Method Coupling Global Evolutionary and Local Gradient Searches for Solving Aerodynamic Optimization Problems

Aerodynamic shape optimization is frequently complicated and challenging due to the involvement of multiple objectives, large-scale decision variables, and expensive cost function evaluation. This paper presents a bilayer parallel hybrid algorithm framework coupling multi-objective local search and global evolution mechanism to improve the optimization efficiency and convergence accuracy in high-dimensional design space. Specifically, an efficient multi-objective hybrid algorithm (MOHA) and a gradient-based surrogate-assisted multi-objective hybrid algorithm (GS-MOHA) are developed under this framework. In MOHA, a novel multi-objective gradient operator is proposed to accelerate the exploration of the Pareto front, and it introduces new individuals to enhance the diversity of the population. Afterward, MOHA achieves a trade-off between exploitation and exploration by selecting elite individuals in the local search space during the evolutionary process. Furthermore, a surrogate-assisted hybrid algorithm based on the gradient-enhanced Kriging with the partial least squares(GEKPLS) approach is established to improve the engineering applicability of MOHA. The optimization results of benchmark functions demonstrate that MOHA is less constrained by dimensionality and can solve multi-objective optimization problems (MOPs) with up to 1000 decision variables. Compared to existing MOEAs, MOHA demonstrates notable enhancements in optimization efficiency and convergence accuracy, specifically achieving a remarkable 5–10 times increase in efficiency. In addition, the optimization efficiency of GS-MOHA is approximately five times that of MOEA/D-EGO and twice that of K-RVEA in the 30-dimensional test functions. Finally, the multi-objective optimization results of the airfoil shape design validate the effectiveness of the proposed algorithms and their potential for engineering applications.

A Multiple Objective Hybrid Algorithm for Daily Ore Blend in Oil Sands Mines

ABSTRACT Oil sands mining contributes to the Canadian daily oil production by producing 1.617 million barrels per day. Processing oil sands is a complex operation with a critical sensitivity to the properties of the blended ore at the crusher that must follow the slurry pipeline and separation tank requirements. The blend optimisation in oil sands mines is a tedious work performed mostly manually by the mining engineers at the mine sites and requires fine-tuning as shovels move from one block to another in the same mining face. Miscalculations leading to deviation from the target properties cause inevitable economically and operationally expensive issues to the value chain including but not limited to sanding the pipeline, separation tank hick-ups, etc. Herein, we present a hybrid multi-objective algorithm addressing abovementioned issues in daily blending process and providing the operation crew with a clear practical production target at each mining face. The algorithm takes the processing targets as inputs and minimises deviations from each desired target by considering material properties at mining faces, the capacity of trucks, and production rates of active shovels.

An efficient multi-objective scheduling algorithm based on spider monkey and ant colony optimization in cloud computing

Due to easier access, improved performance, and lower costs, the use of cloud services has increased dramatically. However, cloud service providers are still looking for ways to complete users’ jobs at a high speed to increase profits and reduce energy consumption costs. To achieve such a goal, many algorithms for scheduling problem have been introduced. However, most techniques consider an objective in the scheduling process. This paper presents a new hybrid multi-objective algorithm, called SMO_ACO, for addressing the scheduling problem. The proposed SMO_ACO algorithm combines Spider Monkey Optimization (SMO) and Ant Colony Optimization (ACO) algorithm. Additionally, a fitness function is formulated to tackle 4 objectives of the scheduling problem. The proposed fitness function considers parameters like schedule length, execution cost, consumed energy, and resource utilization. The proposed algorithm is implemented using the Cloud Sim toolkit and evaluated for different workloads. The performance of the proposed technique is verified using several performance metrics and the results are compared with the most recent existing algorithms. The results prove that the proposed SMO_ACO approach allocates resources efficiently while maintaining cloud performance that increases profits.

A novel hybrid multi-objective algorithm to solve the generalized cubic cell formation problem

The Generalized Cubic Cell Formation Problem (GCCFP) is a Multi-Objective (MO) optimization problem in manufacturing systems. It aims to find the suitable grouping of machines, products, and workers and their best assignment to manufacturing cells. During the resolution of the MO optimization problems, the decision maker should take decisions about multiple variables in order to optimize several objectives, or to find a good trade-off between the concerned objectives. Depending on the moment when the decision about the suitable configuration is taken, the resolution methods may be devised into (i) priori methods, (ii) interactive methods, and (iii) posteriori methods. In this study, a novel posteriori efficient algorithm called Robust Simulated Annealing-AUGMented ɛ-CONstraint (SA-AUGMECON-R) algorithm is introduced in order to solve the GCCFP. The proposed algorithm combines AUGMECON-R exact method and the simulated annealing meta-heuristic. To evaluate the performance of the algorithm, a comparative study was conducted. Regarding the values of the metrics that are used to evaluate the performance of MO algorithms, SA-AUGMECON-R algorithm provides excellent results comparing with AUGMECON 2, AUGMECON-R and NSGA-II.

A hybrid imperialist competitive algorithm for energy-efficient flexible job shop scheduling problem with variable-size sublots

Variable-size batching method has a higher possibility to obtain the most flexible scheduling schemes, which can be better applied to the manufacturing process. However, changing the sizes and number of sublots will not only increase the energy consumption of the manufacturing process, but also increase the search space of the scheduling schemes. A multi-objective optimization problem is formulated considering the makespan and total energy consumption simultaneously. Then, a two-stage multi-objective hybrid algorithm (HICSA) combining imperialist competitive algorithm (ICA) and simulated annealing algorithm (SAA) is proposed to solve this problem and improve the searching efficiency. The ICA and SAA are used to search for the suitable job sequences with optimal machine assignment and the optimal lot splitting schemes in the two stages, respectively. In order to improve the search efficiency, a novel mapping strategy that turns the discrete space into continuous space is applied. Extensive experiments are conducted and the computational results show that HICSA provides promising results for the problem.

Hybrid multi-objective Harris Hawk optimization algorithm based on elite non-dominated sorting and grid index mechanism

In order to find Pareto optimal solution set uniformly distributed along all objectives, a Hybrid Multi-Objective Harris Hawk Optimization Algorithm (H-MOHHO) was proposed based on elite non-dominated sorting and grid indexing mechanism. In order to maintain and improve the coverage of Pareto optimal solution, a method combining the two terms is adopted to obtain the optimal Pareto optimal solution set. Firstly, a non-dominated ranking mechanism based on elite was used to assign rank and sum to select the best solution set, and then the archived grid index mechanism with update mechanism was used to select the final solution set. This hybrid structure can not only obtain the optimal Pareto solution set but also keep the diversity of the population and improve the effectiveness of solving multi-objective optimization problems. In order to verify the performance of the proposed H-MOHHO algorithm, 22 test functions and 4 multi-objective engineering problems are used for simulation, and four performance indexes are compared with Multi-Objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Ant Lion Optimizer (MOALO), Multi-Objective Salp Swarm Algorithm (MSSA) and Multi-Objective Dragonfly Algorithm (MODA). Experimental results show that the proposed H-MOHHO algorithm has better competitiveness and applicability.

A Hybrid Multi-objective Algorithm for Imbalanced Controller Placement in Software-Defined Networks

A Hybrid Multi-objective Algorithm for Imbalanced Controller Placement in Software-Defined Networks

Optimal planning of uncertain renewable energy sources in unbalanced distribution systems by a multi‐objective hybrid PSO–SCO algorithm

High penetration of Renewable Energy Sources into unbalanced distribution systems faces many challenges due to the uncertainty nature of both Renewable Energy Sources and loads as well as the unbalance degree of the distribution systems. This paper proposes the planning of Renewable Energy Sources in unbalanced distribution systems. Different types of Renewable Energy Sources, photo-voltaic and Wind are considered. A multi-objective optimisation problem is formulated using non-dominated sort and crowing distance for multi objective hybrid algorithm that combines the merits of Particle Swarm Optimisation and Sine Cosine Optimisation algorithms. Three objectives, improving voltage profile, power losses minimisation, and minimisation of voltage unbalance, are considered. The proposed method determines the optimal Renewable Energy Sources' specifications such as site/size/type of distributed generators, number of photo-voltaic modules or wind turbines in each distributed generator and their power factor and connection phases. Also, it determines the optimal setting of substation tap changer and system voltage regulators. Moreover, both Renewable Energy Sources and loads uncertainty are considered and modelled using Monte Carlo simulations. An improved matrix-based backward–forward load flow method is developed by using coefficient matrices. The proposed procedure is conducted on IEEE 13-node, 37-node, and 123-node unbalanced distribution systems. The proposed algorithm results compared with individual optimisers show significant performance improvements. The optimal plans resulted by using the proposed hybrid algorithm achieve a reduction in power losses that reached 89% in some cases. Also, the system unbalance index is reduced with a ratio reached 34%. Large improvement in the system voltages profile all over the year is achieved as the system voltage profile index is reduced by a ratio reached 70%.

A hybrid multi-objective bi-level interactive fuzzy programming method for solving ECM-DWTA problem

Electronic countermeasure (ECM) has become one of the most significant factors in modern warfare, in the course of combat, the electronic jamming allocation tasks need to be flexibly adjusted with the change of combat stage, which puts forward higher requirements for the modeling and solution method of this kind of problems. To solve the ECM dynamic weapon target assignment (ECM-DWTA) problem, a hybrid multi-target bi-level programming model is established. The upper level takes the sum of the electronic jamming effects in the whole combat stage as an optimization objective, and locally optimizes the ECM weapon (ECM-WP) assignment scheme in each stage. The lower level takes the importance expectation value of the target subjected to interference and combat consumption as double optimization objectives to globally optimize the ECM-WP assignment scheme. Focus on solving this complex model, a hybrid multi-objective bi-level interactive fuzzy programming algorithm (HMOBIF) is proposed, in this method, exponential membership function is used to describe the satisfaction degree of each level. When solving the multi-objective optimization problem composed of membership functions in the upper and lower levels, we use the MOEA/D algorithm to obtain the Pareto Front (PF) solution set, and then each solution in PF is evaluated and selected by the TOPSIS multi-criteria evaluation method. This local and global interactive optimization process of bi-level model is actually the process of executing observation-orientation-decision-action loop in practical combat. According to the current example, we conduct numerical simulation on the parameters in the model and obtain the parameter values suitable for the model solution. The computational experiments on different scale ECM-DWTA problems show that HMOBIF method is superior to four bi-level programming algorithms in terms of performance index, and can better solve ECM-DWTA problems.

An Application on Aerospace Composite Manufacturing of a Hybrid Multi-Objective Artificial Bee Colony Algorithm

An Application on Aerospace Composite Manufacturing of a Hybrid Multi-Objective Artificial Bee Colony Algorithm

An Application on Aerospace Composite Manufacturing of a Hybrid Multi-Objective Artificial Bee Colony Algorithm

An Application on Aerospace Composite Manufacturing of a Hybrid Multi-Objective Artificial Bee Colony Algorithm

Join queries optimization in the distributed databases using a hybrid multi-objective algorithm

Join queries optimization in the distributed databases using a hybrid multi-objective algorithm

A Novel Hybrid Approach for Multi-Objective Bi-Clustering in Microarray Data

Background: Today bi-clustering technique plays a vital role to analyze gene expression data in microarray technology. This technique performs clustering on both rows and columns of expression data simultaneously. It determines the expression level of genes set under the subset of several conditions or samples. Basically, obtained information is collected in the form of a sub matrix comprising of microarray data that satisfy coherent expression patterns of subsets of genes with respect to subsets of conditions. These sub matrices are represented as bi-clusters and overall process is called bi-clustering. In this paper, we proposed a new meta-heuristics hybrid ABC-MWOA-CC which is based on artificial bee colony (ABC), modified whale optimization algorithm (MWOA) and Cheng and Church (CC) algorithm to optimize the extracted bi-clusters. In order to validate this algorithm, we also delve into finding the statistical and biological relevancy of extracted genes with respect to various conditions. However, most of the bi-clustering techniques do not address the biological significance of genes belonging to extracted bi-clusters Objective: The major aim of the proposed work is to design and develop a novel hybrid multi-objective bi-clustering approach for in microarray data to produce desired number of valid bi-clusters. Further, these extracted bi-clusters are to be optimized to obtain optimal solution. Method: In the proposed approach, a hybrid multi-objective bi-clustering algorithm which is based on ABC along with MWOA is recommended to group the data into desired number of bi-clusters. Further, ABC with MWOA multi-objective optimization algorithm is applied in order to optimize the solutions using variety of the fitness functions. Results: In the analysis of the result, the multi-objective functions which are employed to judge the fitness calculation like Volume Mean (VM), Mean of Genes (GM), Mean of Conditions (CM) and Mean of MSR (MMSR) leads to improve the performance analysis of the CC bi-clustering algorithm on real life data set such as Yeast Saccharomyces cerevisiae cell cycle gene Expression datasets. Conclusion: The effectiveness of the ABC-MWOA-CC algorithm is comprehensively demonstrated by comparing it with well-known traditional ABC-CC, OPSM and CC algorithm in terms of VM, GM, CM and MMSR.

A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty

The timetabling problem (TTP) and vehicle scheduling problem (VSP) are two indispensable problems in public transit planning process. They used to be solved in sequence; hence, optimality of resulting solutions is compromised. To get better results, some integrated approaches emerge to solve the TTP and VSP as an integrated problem. In the existing integrated approaches, the passenger comfort on bus and the uncertainty in the real world are rarely considered. To provide better service for passengers and enhance the robustness of the schedule to be compiled, we study the integrated optimization of TTP and VSP with uncertainty. In this paper, a novel multiobjective optimization approach with the objectives of minimizing the passenger travel cost, the vehicle scheduling cost, and the incompatible trip-link cost is proposed. Meanwhile, a multiobjective hybrid algorithm, which is a combination of the self-adjust genetic algorithm (SGA), large neighborhood search (LNS) algorithm, and Pareto separation operator (PSO), is applied to solve the integrated optimization problem. The experimental results show that the approach outperforms existing approaches in terms of service level and robustness.

A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources.

The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have improved stability and more reliable planning in reactive power (RP) planning. In addition, in modern power systems, renewable resources have an inevitable effect on power system planning. Therefore, wind resources make a complicated problem of planning due to conflicting functions and non-linear constraints. This confliction is the stochastic nature of the cost, loss, and voltage functions that cannot be summarized in function. A multi-objective hybrid algorithm is proposed to solve this problem by considering the linear and non-linear constraints that combine particle swarm optimization (PSO) and the virus colony search (VCS). VCS is a new optimization method based on viruses’ search function to destroy host cells and cause the penetration of the best virus into a cell for reproduction. In the proposed model, the PSO is used to enhance local and global search. In addition, the non-dominated sort of the Pareto criterion is used to sort the data. The optimization results on different scenarios reveal that the combined method of the proposed hybrid algorithm can improve the parameters such as convergence time, index of voltage stability, and absolute magnitude of voltage deviation, and this method can reduce the total transmission line losses. In addition, the presence of wind resources has a positive effect on the mentioned issue.

A Hybrid Multi-Objective Bat Algorithm for Solving Cloud Computing Resource Scheduling Problems

To improve the service quality of cloud computing, and aiming at the characteristics of resource scheduling optimization problems, this paper proposes a hybrid multi-objective bat algorithm. To prevent the algorithm from falling into a local minimum, the bat population is classified. The back-propagation algorithm based on the mean square error and the conjugate gradient method is used to increase the loudness in the search direction and the pulse emission rate. In addition, the random walk based on lévy flight is also used to improve the optimal solution, thereby improving the algorithm’s global search capability. The simulation results prove that the multi-objective bat algorithm proposed in this paper is superior to the multi-objective ant colony optimization algorithm, genetic algorithm, particle swarm algorithm, and cuckoo search algorithm in terms of makespan, degree of imbalance, and throughput. The cost is also slightly better than the multi-objective ant colony optimization algorithm and the multi-objective genetic algorithm.

A hybrid multi-objective algorithm to solve a cellular manufacturing scheduling problem with human resource allocation

A cellular manufacturing system (CMS) is a suitable system for the economic manufacture of part families. Scheduling the manufacturing cells plays an effective role in successful implementation of the manufacturing system. Due to the fact that in the CMS, bottleneck machine and human resources are two important factors, which so far have not been studied simultaneously in a mathematical model, there should be a model to consider them. Therefore, this research develops a bi-objective model for CMS in a three-dimensional space of machine-part and human resources. The main objective is to minimize the maximum completion time of all tasks in the system and reduce the number of intercellular translocation based on bottleneck machines’ motion and human resources. Due to the NP-hardness of the studied problem, applying the conventional solution methods is very time-consuming, and is impossible in large dimensions. Therefore, the use of metaheuristic methods will be useful. The accuracy of the proposed model is investigated using LINGO by solving a small example. Then, to solve the problem in larger dimensions, a hybrid Multi-Objective Tabu Search-Genetic Algorithm (MO-TS-GA) is designed and numerical results are reported for several examples.