Modified Multi-objective Particle Swarm Optimization Research Articles

A modified multi-objective particle swarm optimization (M-MOPSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system

This study proposes and utilizes a modified multi-objective particle swarm optimization (M-MOPSO) algorithm for the optimal sizing of a solar-wind-battery hybrid renewable energy system for a rural community in Rivers State, Nigeria. Unlike previous studies that primarily focused on minimizing total economic cost (TEC) and total annual cost (TAC), this research emphasizes minimizing the loss of power supply probability (LPSP) and levelized cost of energy (LCOE). The M-MOPSO algorithm introduces a dynamic inertia weight, a unique repository update mechanism, and a dominance-based personal best update strategy, which collectively enhance its performance. Comparative analysis with PSO, NSGA-II, MOPSO and hybrid GA-PSO demonstrates that M-MOPSO consistently achieves a lower LPSP, although its LCOE remains higher. The M-MOPSO optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient condition.

A Fuzzy Bi-objective Mathematical Model for Perishable Medical Goods Supply Chain Network Considering Crisis Situations: An Empirical Study.

In case of crisis, the salvation of injuries depends on the timely provision of medical goods, relief supplies, and equipment. The aim of this study is to present a mathematical model for the supply chain network of perishable medical goods in crisis situation considering the uncertain environment. In this paper, a three-level supply chain including suppliers, intermediate warehouses, and final customers is developed for perishable medical items. The uncertainty of customer demand for service and the spent time in the intermediate warehouses are considered using the exponential distribution functions. Also, it is assumed that the life-cycle of perishable medical goods follow the Weibull distribution function. The model attempts to minimize the total costs of the supply chain and total presence time of perishable items in the whole chain. The LP-Metric method is employed for solving small-sized problems. Due to the NP-Hardness of the problem, the modified Multi-objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm (NSGA-II) are utilized as 2 well-known and efficient meta-heuristic algorithms for solving large-sized problems. The findings indicate that the meta-heuristic algorithms are efficient in achieving close to the optimal solution for large-size problems in a reasonable time. Also, the results demonstrate that NSGA-II outperforms MOPSO in terms of the high quality solution. Finally, the applicability of the model to real-world problems is demonstrated using a real case study. This paper can assist the planners and decision-makers of perishable drugs supply chain networks in crisis conditions with on-time supplying and distributing the required emergency items.

Multi Objective Salp Swarm based Energy Efficient Routing Protocol for Heterogeneous Wireless Networks

Routing is a persistent concern in wireless sensor networks (WSNs), as getting data from sources to destinations can be a tricky task. Challenges include safeguarding the data being transferred, ensuring network longevity, and preserving energy in harsh environmental conditions. Consequently, this study delves into the suitability of using multi-objective swarm optimization to route heterogeneous WSNs in the hope of mitigating these issues while boosting the speed and accuracy of data transmission. In order to achieve better performance in terms of load balancing and reducing energy expenditure, the MOSSA-BA algorithm was developed. This algorithm combines the Multi-Objective Salp Swarm Algorithm (MOSSA) with the exploiting strategy of the artificial bee colony (BA) in the neighbourhood of Salps. Inspired by the SEP and EDEEC protocols, the integrated solutions of MOSSA-BA were used to route two and three levels of heterogeneous networks. The embedded solutions provided outstanding performance in regards to FND, HND, LND, percentage of remaining energy, and the number of packages delivered to the base station. Compared to SEP, EDEEC, and other competitors based on MOSSA and a modified multi-objective particle swarm optimization (MOPSO), the MOSSA-BA-based protocols demonstrated energy-saving percentages of more than 34% in medium-sized areas of interest and over 22% in large-sized areas of detection.

Voltage Stability Improvement in Optimal Placement of Voltage Regulators and Capacitor Banks Based on FSM and MMOPSO Approach

Installation of Shunt Capacitor Banks (SCBs) and Voltage Regulators (VRs) within distribution system is one of the most effective solutions in reactive power control for improving the voltage profile and reducing power losses along the feeder. However, the presence of the VRs can deteriorate the Voltage Stability Margin (VSM) in distribution feeders. To address this issue, this paper proposes a multi-objective programming model for the simultaneous optimal allocation of VRs and SCBs in the distribution network to improve the voltage profile and to minimize power losses and installation costs. In the proposed model, a Voltage Stability Index (VSI) is considered to prevent voltage instability during SCBs/VRs allocation. A new Modified Multi-Objective Particle Swarm Optimization (MMOPSO) algorithm which includes a dynamic inertia weight and mutation operator is proposed to obtain the optimal solutions as a Pareto set. Thereinafter, a Fuzzy Satisfaction Method (FSM) determines the optimal solution. A practical long radial distribution feeder has been employed to demonstrate the efficiency and efficacy of the proposed model along with a comparison between the proposed MMOPSO and the original MOPSO.

Energy-efficient multi-objective flexible manufacturing scheduling

This paper presents a novel scheduling of a resource-constrained Flexible Manufacturing System (FMS) with consideration of the following sub-problems: (i) machine loading and unloading, (ii) manufacturing operation scheduling, (iii) machine assignment, and (iv) Automated Guided Vehicle (AGV) scheduling. In the proposed model, both the AGV and machinery are considered as the required resources. Energy efficiency of AGVs has been studied in order to improve environmental sustainability in terms of a linear function, which is based on load and distance, accordingly. Because of the NP-hard characteristics of the problem, a modified multi-objective particle swarm optimization (MMOPSO) has been developed for solving the model and compared with the classic version of the multi-objective particle swarm optimization (MOPSO) algorithm in terms of five performance metrics. Finally, the results are evaluated by the application of a multi-criteria decision-making (MCDM) algorithm according to which the MMOPSO outperforms the MOPSO.

Optimized Operation of Hybrid System Integrated With MHP, PV and PHS Considering Generation/Load Similarity

Renewable energy is widely used as a clean energy in the world, yet the intermittent of the power generation from renewable energy power plants, resulting in poor quality of power supply. However, the hybrid energy system and some energy storage devices can be installed to mitigate the power fluctuation, to achieve power smoothing and maximize the profit, a double-objective optimization model of hybrid system integrated with micro-hydro power (MHP), photovoltaic (PV), and pumped hydro storage (PHS) is established in this study. The day-ahead optimized operation strategy of the PHS based-on hour-level data of seven days is obtained, which aims to maximize the similarity value (SIM) between the power generation curve and load profile, and the economic revenue (ER) of the PHS. Optimized schedule is obtained by means of chance-constrained programming (CCP) and modified multi-objective particle swarm optimization (MOPSO). The performance of the model is evaluated by using the meteorological, the historical MHP power generation and the load data obtained in Xiaojin County, Sichuan province, China. The simulation results reveal that the best trade-off value between SIM and ER can be obtained on the Pareto front with comprehensive consideration of the system operation strategy and target. Furthermore, the generation/load similarity can be increased 7.89% under the participating of the PHS.

Bi-objective optimization for integrating quay crane and internal truck assignment with challenges of trucks sharing

Due to both the rapid growth of world and the expansion of the flow of container shipment, a maritime container terminal plays a vital role in global coverage of supply chain. In this market, because shipping is growing fast, container terminals should be able to serve the vessels in the shortest possible time. Alternatively, the limited availability of operational facilities, such as internal trucks, makes servicing more complicated in container terminals. Therefore, this study aimed to integrate the assignment of quay cranes in container terminals and internal truck sharing assignment among them. For this purpose, a bi-objective optimization model is developed. In the proposed model, several assignment phases, including the assignments of the vessel to container terminals, cranes to terminals, cranes to vessels and trucks to cranes are performed. The model also seeks to increase and improve the efficiency and effectiveness of internal trucks by sharing them among different terminals, so that there is an appropriate balance between the volume of workloads of the terminals and the trucks in question. The first objective function in the proposed model seeks to minimize operational costs and the second objective function seeks to minimize the maximum overflowed workload in the terminals. Furthermore, in order to solve the proposed model, two meta-heuristic multi-objective algorithms, including modified non-dominated sorting genetic algorithm-II (MNSGA-II) and modified multi-objective particle swarm optimization (MMOPSO) are presented. Several numerical examples have been investigated and analyzed to show the accuracy of the proposed model and the methods. In addition, the results demonstrated that the simultaneous consideration of the assignments and the sharing of trucks would reduce the remaining workload in the terminals.

A modified multi-objective sorting particle swarm optimization and its application to the design of the nose shape of a high-speed train

Based on the concepts of niche count and crowding distance, a modified multi-objective particle swarm optimization (MPSO) is introduced. The niche count and crowding distance are used to determine the globally best particle across four test cases using an external file. A comparative analysis was carried out between MPSO and non-dominated sorting multi-objective adaptive genetic algorithms, both real-coded and binary-coded. The results show that MPSO based on the crowding distance is best for getting the Pareto front, especially for problems with high-dimensional and non-continuous Pareto fronts. In order to verify the efficiency of MPSO in solving engineering problems, the optimal design of the aerodynamic nose shape of high-speed trains was undertaken using a modified vehicle modeling function (MVMF) parametric method. Taking the aerodynamic drag of the whole train (Cd) and the aerodynamic lift of the trailer car (Cl) as the optimization goals, the Kriging surrogate model was introduced to reduce the computational time, and the MPSO based on crowding distance was used to find the Pareto front. The optimization results show that MPSO is efficient at getting the Pareto front; compared to the original shape, the Cd and Cl of the optimal shape are reduced by 1.6% and 29.74%, respectively.

Shortest distance estimation in large scale graphs

Purpose – Shortest distance query between a pair of nodes in a graph is a classical problem with a wide variety of applications. Exact methods for this problem are infeasible for large-scale graphs such as social networks with hundreds of millions of users and links due to their high complexity of time and space. The purpose of this paper is to propose a novel landmark selection strategy which can estimate the shortest distances in large-scale graphs and clarify the efficiency and accuracy of the proposed strategy in comparison with currently used strategies. Design/methodology/approach – Different from existing strategies, the landmark selection problem is regarded as a binary combinational optimization problem consisting of two optimization objectives and one constraint. Further, the original binary combinational optimization problem with constraints is transformed to a proper form of optimization objectives without any additional constraints and the equivalence of solutions is proved. Finally the solution of the optimization problem is performed with a modified multi-objective particle swarm optimization (MOPSO) integrating the mutation operator and crossover operator of genetic algorithm. Findings – Four real networks of large scale are used as data sets to carry out the experiments and the experiment results show that the proposed strategy improves both of the accuracy and time efficiency to perform shortest distance estimation in large scale graph compared to other currently used strategies. Originality/value – This paper proposes a novel landmark selection strategy which regards the landmark selection problem as a binary combinational optimization problem. The original binary combinational optimization problem with constraints is transformed to a proper form of optimization objectives without constraints and the equivalence of these two optimization problems is proved. This novel strategy also utilizes a modified MOPSO integrating the mutation operator and crossover operator of genetic algorithm.

Balancing risk and cost in fuzzy economic dispatch including wind power penetration based on particle swarm optimization

Utilization of renewable energy resources such as wind energy for electric power generation has assumed great significance in recent years. Wind power is a source of clean energy and is able to spur the reductions of both consumption of depleting fuel reserves and emissions of pollutants. However, since the availability of wind power is highly dependent on the weather conditions, the penetration of wind power into traditional utility grids may incur certain security implications. Therefore, in economic power dispatch including wind power penetration, a reasonable tradeoff between system risk and operational cost is desired. In this paper, a bi-objective economic dispatch problem considering wind penetration is formulated, which treats operational costs and security impacts as conflicting objectives. Different fuzzy membership functions are used to reflect the dispatcher’s attitude toward the wind power penetration. A modified multi-objective particle swarm optimization (MOPSO) algorithm is adopted to develop a power dispatch scheme which is able to achieve compromise between economic and security requirements. Numerical simulations including sensitivity analysis are reported based on a typical IEEE test power system to show the validity and applicability of the proposed approach.