An Improved Strength Pareto Evolutionary Algorithm 2 Based on Markov Networks

The upsurge of technological opportunities has brought about the speedy growth of the industry’s processes and machineries. The increased size and complexity of these systems, followed by high dependence on them, have necessitated the need to intensify the maintenance processes, requiring more effective maintenance scheduling approach to minimise the number of failure occurrences; which could be realised through a well-articulated perfect preventive maintenance with component replacement (PPMR) schedules from infancy stage through to completion stage. Then, using Strength Pareto Evolutionary Algorithm 2 (SPEA2), we devise a multi-objective optimisation approach that uses dependability and cost as objective functions. Typically, due to large scale problems, SPEA2 implementation on a single node proves to be computationally challenging, taking up to 1 h 20 min to run an instance of SPEA2 PPMR scheduling optimisation, the search time, the quality of the solution and its accuracy suffer much. We address this limitation by proposing a distributed architecture based on MapReduce, which we superimpose on SPEA2. The evaluation of our approach in a case study presents the following results: (1) that our approach offers an effective optimisation modelling mechanism for PPMR; (2) that the distributed implementation tremendously improves the performance by 79% computational time of the optimisation among 4 nodes (1 master-node and 3 worker-nodes) and the quality and accuracy of the solution without introducing much overhead.

problems. Many different experiments have shown that the evolutionary algorithm Strength Pareto Evolutionary Algorithm 2 (SPEA2) outperforms other approaches, and this makes it an excellent candidate for inclusion in the final design. According to Strength, one of SPEA2's core concepts, the population is sorted into niches based on the Pareto Front idea. With regard to outcomes this technique has a flaw that is mitigated by adding a fitness density estimator. Weakness in Strength is addressed with an approach called Strength by objective, which aims to include solution who do not dominate or are dominated by others inside the process. In this paper, the results will show a clear superiority of the proposed method comparing with the original method in solving the multi-objective Knapsack problem using three sizes of the Knapsack and the problem size of 750 items which is generated randomly. The comparison results between the proposed method and the other algorithms show outperformance of the Proposed algorithm using the dominance inductor showed the superiority of the proposed algorithm by a percentage of more than 1 %, Thanks to a highly diverse population and the inclusion of solutions that can be improved, the (SPEA2) algorithm's performance has been vastly enhanced by the Objective sorting mechanism

• VTPS Efficiency: The vane-type pipe separator (VTPS) is effective for compact, high-efficiency oil-water separation in constrained oilfield environments. • Advanced Modeling: A computational framework using the AIAD model within an Euler-Euler multiphase context is developed and experimentally validated. • Key Parameters Identified: Sensitivity analysis reveals guide vane angle and oil fraction as dominant factors affecting pressure drop and oil recovery. • Optimization Approach: Multi-objective optimization using SPEA2 and TOPSIS identifies the best trade-off between oil recovery and hydraulic loss. • Optimal Design Achieved: An optimal VTPS setup (25.4° vane angle, 3.57 m³/h flow rate, 18.4% oil fraction, 0.12 split ratio) achieves a 0.162 oil recovery factor at 2422 Pa pressure drop. Efficient oil-water separation is increasingly vital in mature oilfields, where enhanced water production challenges conventional downhole separation technologies. The vane-type pipe separator (VTPS) offers a promising solution due to its compact configuration and superior phase separation efficiency in constrained environments. However, the inherent complexities of swirling multiphase flow necessitate advanced modeling approaches for performance optimization. In this study, a comprehensive computational framework based on the Algebraic Interfacial Area Density (AIAD) model within an Euler-Euler multiphase context is developed and validated against benchmarked experimental data. Sensitivity analysis employing response surface methodology (RSM) and global-local indices quantifies the influence of key decision parameters, including guide vane installation angle, inlet flow rate, oil fraction, and split ratio, on separator performance. The study elucidates the parametric interplay affecting pressure drop and oil recovery, revealing guide vane angle and oil fraction as the dominant factors for hydraulic and separation performance, respectively. A multi-objective optimization is conducted using the Strength Pareto Evolutionary Algorithm 2 (SPEA2), with final selection by TOPSIS, to maximize oil recovery while minimizing hydraulic losses. The proposed methodology identifies an optimal configuration with a 25.4° vane angle, 3.57 m³/h flow rate, 18.4% oil fraction, and 0.12 split ratio, achieving a 0.162 oil recovery factor at a pressure drop of 2422 Pa. These findings provide actionable guidelines for VTPS design and operation, facilitating more efficient separation in oilfield applications and demonstrating the AIAD model's efficacy for industrial-scale multiphase flow optimization.

Software as a service (SaaS) is the main service model in cloud computing and has been generally recognized. More and more traditional software turns to SaaS applications. For SaaS provider, it hopes that it can provide better service performance to tenants while attain more profit. But the two goals are contradictory. So in this paper, we model the SaaS application service provisioning problem as multi-objective optimization problem and propose a service provisioning strategy based on Strength Pareto Evolutionary Algorithm 2 (SPEA2) for SaaS applications in cloud. The experiments show that compared with random strategy and greedy strategy, the service provisioning strategy proposed in this paper can effectively improve the service performance and profit.

SPEA2 (Strength Pareto Evolutionary Algorithm 2) is an evolutionary algorithm based on population, which is solutions to resolve Multi-objective Optimization Problems (MOPs). It has selection process that to select from dataset or objective problem benchmarking such as DTLZ. Our aim is to improve SPEA2 performance to process a population using parallelism in GPU. By optimization fitness and distance calculations using transposed data to process in CUDA platform. The result shows that speed up increase approximately 1.5 times.

SummaryInternet of Things (IoT) is a platform which connects the objects through the Internet. The IoT devices have limited processing capabilities. This makes the processing and analyzing huge amount of data as a challenging task in IoT environment. Due to this disadvantage of IoT devices, it cannot be used in many useful applications where they are mostly required. Hence, a technique which helps in overcoming this disadvantage is mostly desired. One of the desired technologies is the cloud computing. The cloud computing has rich set of resources, and it is efficient in processing and analyzing data irrespective of its type and quantity. The services of the cloud will be provided by a cloud service provider (CSP). Selecting an appropriate cloud service provider is a major issue in an integrated environment of IoT and Cloud. Therefore, the focus of this paper is on cloud collaboration with IoT environment with suitable CSPs. A multi‐objective genetic algorithm to find the optimized solution is proposed in this paper. The Non‐dominated Sorting Genetic Algorithm 2 (NSGAII) and Strength Pareto Evolutionary Algorithm 2 (SPEA2) are used to analyze the performance of the proposed algorithm. The Profit Information (PI) to the individual and Trust Relationship (TR) value are considered as the objectives for selecting the CSPs. The results demonstrated that the proposed Multi‐objective Genetic Algorithm (MGA) algorithm along with NSGAII performs more efficiently than MGA algorithm with SPEA2.

This paper proposed a multiobjective genetic algorithm approach for solving process planning and scheduling (PPS) problems in a distributed manufacturing system (DMS) environment. In this environment, factories processing various machines and tools at different geographical locations are often combined to produce various parts with different resource constraints. This paper proposed a fast multiobjective genetic algorithm with archive (fmoGA‐A) mechanism to deal with PPS problem with considering the minimization of the maximum total processing time and minimization of the maximum variation of workload of machine in DMS. The proposed algorithm has been compared with other approaches to testify and benchmark the optimization reliability on several PPS problems. These comparisons demonstrate the importance of PPS in DMS and indicate fmoGA‐A is better than adaptive weight approach genetic algorithm (awaGA) did on efficacy and efficient; the efficacy is not less than non‐dominated sorting genetic algorithm II (NSGA‐II) and strength Pareto evolutionary algorithm 2 (SPEA2) and the efficient is better than NSGA‐II and SPEA2. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Solving a tri-objective supply chain problem with modified NSGA-II algorithm

Multi-objective planning of electrical distribution systems incorporating sectionalizing switches and tie-lines using particle swarm optimization

Logistics network design is one of the principal parts of strategic decisions in the planning and control of production systems. It deals with determining the warehouses locations and the definition of product flow between facilities and clients. This work is focused in finding an approximation of the Pareto-optimal front for two conflicting objective functions in logistic networks design: minimize costs and maximize coverage. Since the establishing of which warehouses must be opened constitute a combinatorial optimization problem, two metaheuristic techniques, namely Improved Strength Pareto Evolutionary Algorithm - SPEA2 and a novel binary version of Bacterial Chemotaxis Multi-objective Optimization Algorithm - BCMOA, were applied. With the aim of finding the optimal flow between clients and warehouses, network flow algorithms were also used. The performances of the above techniques were evaluated by comparative analysis of the results obtained in the solution of eight randomly generated problems by means of the dominated hypervolume metric (S-metric). The hybrid methodology here developed to solve the logistics network design problem - which combines metaheuristic techniques with a network flow algorithms - showed to be competitive regarding the Pareto Optimal Front approximation, and also displayed high efficiency in execution times.

This paper proposes an innovative hybrid multi-objective (MO), fast converging optimization algorithm, enabling global optimum tracking in permanent magnet traction motor design. The methodology developed is based on Artificial Bee Colony (ABC) technique, strength pareto evolutionary algorithm (SPEA2) and Differential Evolution (DE) strategy ensuring fast and reliable convergence to the optimal Pareto front. An application example concerning an unequal teeth surface mounted permanent magnet wheel motor design is given.

This paper furthers the development of Genetic Algorithms (GAs) and their application to the design of multi-mission radar waveforms. An application was developed with the goal of developing a waveform suite that finds the Pareto optimal solutions to a multi-objective optimization radar problem. Utilizing the Strength Pareto Evolutionary Algorithm 2 (SPEA2) a series of radar parameters are optimized along the fitness metrics of interest. This implementation builds upon the previous work of [1] to develop an application that is capable of analyzing longer more realistic scenarios. It also advances the previous research by solving for the Pareto optimal front of a simultaneous Synthetic Aperture Radar (SAR) and Moving Target Indication (MTI) mission. These preliminary results are presented to validate the performance of the new application against previous work and introduce some results of the multi-mission radar suite.

Enhanced Genetic Algorithm based computation technique for multi-objective Optimal Power Flow solution

Multi-objective optimization of hydrocyclones using meta-heuristic algorithms and preference-informed decision-making

Optimal location, number, and settings of unified power flow controllers (UPFC) using various multi-objective optimization algorithms is presented in this paper. The UPFC parameters, locations and number are computed to maximize the voltage stability margin and minimize the real power losses at the same time. For this, developed hierarchical optimization versions of three recent multi-objective algorithms are proposed namely: non-dominated genetic algorithms (NSGA-II), non-dominated sorting particle swarm optimization (NSPSO) and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The fuzzy logic is proposed to extract the best compromise solution from the Pareto set. The proposed algorithms are applied to IEEE 30-bus power system. The line flow and load bus voltage limits are taken into account. The obtained results show that the installation of the UPFC in the power system minimizes the power losses, enhances the static voltage stability, and improves the voltage profiles. Furthermore, the proposed methods are able to solve a hard discrete---continuous constrained multi-objective optimization problem. In addition, they do not show any limitation on the number of objective functions under consideration.