Hybrid Multi-objective Optimization Research Articles

Strategic Integration of DG and ESS by using Hybrid Multi Objective Optimization with Wind Dissemination in Distribution Network

The importance of distributed generation (DG) has increased recently as a result of the growth in commercial and industrial loads, which has put more pressure on conventional energy sources and utilities. Alternative power generation methods that can handle the massive load without endangering the environment are therefore urgently needed. The installation of Energy Storage Systems (ESSs) may give a substantial opportunity to enhance the aesthetic appeal of the distribution system. DG is a practical substitute for conventional energy sources, which have drawbacks for both the economics and the environment. It goes without saying that there are situations in which a large amount of land and money are required. However, improper DG location or sizing increases safety issues because to the increasing power loss caused by larger reverse flow from the load to the supply. In order to reduce power losses and maintain voltage stability, the ideal location and dimensions for the DG and ESS in IEEE Radial Distribution systems are evaluated. To accomplish these objectives, a hybrid Shuffled Frog Leap Algorithm (SFLA) and Improved Firefly Algorithm (SFLA-IFFA) is suggested. The SFLA-IFFA produced better results in terms of the ideal location and DG size when compared to other existing algorithms.

Multi-Objective Hybrid Optimization for Optimal Sizing of a Hybrid Renewable Power System for Home Applications

An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development. This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO). The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability. A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method. The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and a higher standard deviation. The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.12% can be achieved. The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions.

Proposed association rule hiding based privacy preservation model with block chain technology for IoT healthcare sector

The purpose of this study is to improve healthcare system performance by utilizing cutting-edge computing technologies like blockchain and the Internet of Things. Blockchain-based data transfer, Association Rule hiding, and ideal key generation are the three primary aspects of the proposed work. Initially, data are altered using blockchain, then the data enter the Proposed Association Rule concealing stage. In this research a novel association rule concealment phase is implemented, which has three crucial processes: (1) data pattern mining using the improved apiori algorithm, (2) detection of sensitive data based on the improved apiori algorithm, and (3) a method for cleaning and restoring data. Using the generated optimal key, the sanitized sensitive data are recovered. Keys are critical to both the data sanitization and restoration procedures. Hence, a multi-objective hybrid optimization model is known as the Rock Hyraxes Updated Marriage in Honey Bee Optimization (RHUMBO) is employed. Then, the confidentiality of the suggested model’s performance has been validated. From the experimental analysis the proposed model achieved 97% for Cleveland dataset at 90th learning percentage which is the best score. And the cost function of the suggested model is minimum (∼0.08 at 100th iteration).

A multi-objective optimization of FCL and DOCR settings to mitigate distributed generations impacts on distribution networks

Despite providing increased reliability and power quality in meeting the load demand, the integration of DGs has imposed challenges on the protection system. High penetration of DGs changes the overall network impedance and increases the fault current level in the network. Consequently, some buses are exposed to critical conditions, violate the circuit breakers (CB) handling capacity, and affect the existing relay coordination settings. To mitigate these adverse impacts, fault current limiters (FCL) can be a potential solution to minimize the short circuit current within permissible switchgear-rated limits. Due to its expensive cost, it is crucial to ensure the optimal FCL placement and sizing, and at the same time maintain the effectiveness of the protection performance during various network operation states. This research formulates FCL and directional overcurrent relay (DOCR) settings as a combined protection coordination scheme optimized by a multi-objective hybrid optimization technique. The proposed formulation aims to determine the minimal FCL sizing with minimum investment cost to satisfy the relay coordination constraints at high fault-level buses, irrespective of DG locations and network operating state. User-defined relay characteristics (UDC) are employed to intensify the DOCR coordination performance and obtain minimum operating time, where the conventional inverse relay constants (A&B) are optimized with (TSM & Ipu). The combined UDC relay settings and FCL problem formulation solution attained by MO-hybrid optimization can lessen the escalated electromagnetic stresses, costly switchgear replacement, and relay maloperation. The performance of the proposed technique is assessed by implementing it on the radial (IEEE-33 Bus) and meshed (IEEE 30-Bus) DNs with optimized FCL sizing and relay settings. The optimal results demonstrate the effectiveness of the proposed technique in maintaining the relay coordination performance in the presence of DGs and FCL under an ON/OFF grid connection.

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.

Adaptive approximation-based multi-objective hybrid optimization method for dual-gradient top-hat structures

In this article, an adaptive approximation-based multi-objective hybrid optimization method, integrating a multi-objective decision-making method, adaptive approximation method and hybrid optimization method, is developed. In particular, a multi-objective decision-making method developed by combining a weight strategy inspired by grey relational analysis and entropy analysis is applied to normalize multiple objectives; the trust region is introduced into the support vector regression model to solve the problem of the sequential sampling for the approximation model; a hybrid optimization algorithm combining the genetic algorithm pattern search algorithm is developed to further assist the surrogate model-based optimization. Finally, a novel dual-gradient structure with graded thickness and graded material property along the axial direction is applied to the crashworthiness optimization design. The results demonstrate that the developed hybrid method has faster optimization efficiency than the Pointer algorithm built into Isight commercial software, and the final optimized structure is superior to the original one.

Trust and Energy Based Multi-Objective Hybrid Optimization Algorithm for Wireless Sensor Network

Trust and Energy Based Multi-Objective Hybrid Optimization Algorithm for Wireless Sensor Network

Parametric analysis through ANFIS modelling and optimization of micro-hole machining in super duplex stainless steel by die-sinking EDM

ABSTRACT Super duplex stainless steel (SDSS) contains ferrite and austenite in equal proportions. The high chromium imparts good corrosion resistance and high-temperature workability but the low thermal conductivity and high work hardening reduces its machinability by traditional methods. In this study, the feasibility of using electric discharge machining (EDM) to create geometrically accurate microholes with high productivity has been explored. Individual effect of input process parameters like pulse-on-Time (Ton), pulse-off time (Toff) and current (I) on material removal rate (MRR), overcut (OC) and taper angle (TA) has been investigated through adaptive neuro fuzzy inference system (ANFIS). Furthermore, hybrid multiobjective optimisation technique of grey relational analysis (GRA) combined with principal component analysis (PCA) was used to determine the optimal process input parameters. During hole sinking µ-EDM, increase of pulse on time increases the MRR, whereas the OC and taper decrease. GRA-PCA optimisation technique yields optimal input parameters which enhances MRR by 5.92% and rescues the OC and TA by 27.1% and 75%, respectively.

Key initiatives to improve the machining characteristics of Inconel-718 alloy: Experimental analysis and optimization

Inconel 718 is a heat-resistant Ni-based superalloy widely used, particularly, in aircraft and aero-engineering applications. It has poor machinability due to its unique thermal and mechanical properties. For this reason, studies have been carried out from past to present to improve the machinability of Nickel-based (Ni) alloys. Further improvement can be achieved by applying hybrid multi-objective optimization strategies to ensure that cutting parameters and cooling/lubrication strategies are also adjusted effectively. That is why, in this research, the machinability of Inconel 718 is optimized under various sustainable lubricating environments i.e., dry medium, minimum quantity lubrication (MQL), nano-MQL, and cryogenic conditions at different machining parameters during end-milling process. Subsequently, the analysis of variance (ANOVA) approach was implanted to apprehend the impact of each machining parameter. Finally, to optimize machining environments, two advanced optimization algorithms (non-dominated sorting genetic algorithm II (NSGA-II) and the Teaching-learning-based optimization (TLBO) approach) were introduced. As a result, both methods have demonstrated remarkable efficiency in machine response prediction. Both methodologies demonstrate that a cutting speed of 90 m/min, feed rate of 0.05 mm/rev, and CO2 snow are the optimal circumstances for minimizing machining responses during milling of Inconel 718.

Hybrid multi-objective method based on ant colony optimization and firefly algorithm for renewable energy sources

This work presents a hybrid multi-objective optimization technique (HMO-ACO/FA) based on ant colony optimization (ACO) and firefly algorithm to address multi-objective optimum power flow (MOOPF) difficulties (FA) by eliminating certain operating restraints. In HMO-ACO/FA, an objective function (OF) vector with a vector of colonies has been modelled to be solved by an ACO, but the spare solutions in the search space have been projected to be found using a local search technique. Besides, FA has been projected to enhance the enactment and to obstacle the unfeasible solutions directly by presenting two moves for the fireflies. HMO-ACO/ The IEEE 30-bus standard and the Indian technical 62-bus scheme are both used in India have started FA to address three bi-objective characteristics: energy lower cost and power reducing losses; energy cost and power reduction; and power cost and energy loss reduction and L-index, as well as reductions in energy costs and pollutant emissions, as well as a tri-objective function that simultaneously reduces energy costs, reduces power losses, and improves voltage regulation. The findings were obtained by introducing two renewable energy sources (RES) into the test program, such as solar photovoltaic (PV) and wind farms. The findings show that HMO-ACO/FA can generate precise, MOOPF-free solutions with a well-distributed Pareto optimal distribution. The results of other published techniques in the literature were similar to those of the HMO-ACO/FA algorithms. The analysis' results show that HMO-ACO/FA performs better than other strategies in terms of discrete and compromise responses.

Investigating a citrus fruit supply chain network considering CO2 emissions using meta-heuristic algorithms

AbstractAccording to the increasing carbon dioxide released through vehicles and the shortage of water resources, decision-makers decided to combine the environmental and economic effects in the Agri-Food Supply Chain Network (AFSCN) in developing countries. This paper focuses on the citrus fruit supply chain network. The novelty of this study is the proposal of a mathematical model for a three-echelon AFSCN considering simultaneously CO2 emissions, coefficient water, and time window. Additionally, a bi-objective mixed-integer non-linear programming is formulated for production–distribution-inventory-allocation problem. The model seeks to minimise the total cost and CO+ emission simultaneously. To solve the multi-objective model in this paper, the Augmented Epsilon-constraint method is utilised for small- and medium-sized problems. The Augmented Epsilon-constraint method is not able to solve large-scale problems due to its high computational time. This method is a well-known approach to dealing with multi-objective problems. It allows for producing a set of Pareto solutions for multi-objective problems. Multi-Objective Ant Colony Optimisation, fast Pareto genetic algorithm, non-dominated sorting genetic algorithm II, and multi-objective simulated annealing are used to solve the model. Then, a hybrid meta-heuristic algorithm called Hybrid multi-objective Ant Colony Optimisation with multi-objective Simulated Annealing (HACO-SA) is developed to solve the model. In the HACO-SA algorithm, an initial temperature and temperature reduction rate is utilised to ensure a faster convergence rate and to optimise the ability of exploitation and exploration as input data of the SA algorithm. The computational results show the superiority of the Augmented Epsilon-constraint method in small-sized problems, while HACO-SA indicates that is better than the suggested original algorithms in the medium- and large-sized problems.

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

Hybrid Multi-Objective Optimization Approach With Pareto Local Search for Collaborative Truck-Drone Routing Problems Considering Flexible Time Windows

The collaboration of drones and trucks for last-mile delivery has attracted much attention. In this paper, we address a collaborative routing problem of the truck-drone system, in which a truck collaborates with multiple drones to perform parcel deliveries and each customer can be served earlier and later than the required time with a given tolerance. To meet the practical demands of logistics companies, we build a multi-objective optimization model that minimizes total distribution cost and maximizes overall customer satisfaction simultaneously. We propose a hybrid multi-objective genetic optimization approach incorporated with a Pareto local search algorithm to solve the problem. Particularly, we develop a greedy-based heuristic method to create initial solutions and introduce a problem-specific solution representation, genetic operations, as well as six heuristic neighborhood strategies for the hybrid algorithm. Besides, an adaptive strategy is adopted to further balance the convergence and the diversity of the hybrid algorithm. The performance of the proposed algorithm is evaluated by using a set of benchmark instances. The experimental results show that the proposed algorithm outperforms three competitors. Furthermore, we investigate the sensitivity of the proposed model and hybrid algorithm based on a real-world case in Changsha city, China.

A Multiobjective Hybrid Optimization Algorithm for Path Planning of Coal Mine Patrol Robot

In the complex underground environment, the paths planned for coal mine patrol robot are often too long and unsmooth under the influence of low visibility and poor road conditions. To solve the problems, this paper improves the hybrid algorithm between the improved artificial fish swarm algorithm (AFSA) and the dynamic window algorithm (DWA) for global path planning of coal mine patrol robot and introduces the improved genetic algorithm (GA) to enhance the path planning accuracy. Based on the global optimal path, the improved DWA was adopted to design a new adaptive trajectory evaluation function, which improves the ability of the patrol robot to avoid local obstacles. The proposed optimization algorithm was proved feasible through simulations. In addition, a simulation platform for the control of coal mine patrol robot was established, using the software development platform for coal mine patrol robot and robot operating system (ROS). The simulation results show that the improvement shortened the path length by 0.12 m, reduced the time by 3.14 s, and removed many turning points and redundant points. Therefore, the proposed improved hybrid path planning algorithm is effective and superior.

Multivariate optimization of mechanical and microstructural properties of welded joints by FSW method

This paper investigates the microstructural and mechanical properties in the joining of aluminum sheets using the friction stir welding (FSW) method. First, the microstructural properties are investigated, and then the effect of process parameters on the grain size, defect formation, microhardness, and fracture energy is discussed. The grain size in the stir zone is remarkably refined compared to the base metal, while the microhardness is reduced due to the annealing effects of the FSW. According to the findings, the material integrity in the stir zone is the most critical aspect in determining the value of absorbed energy in the fracture test. The existence of a cavity in the stir zone severely reduces the fracture energy, while the negative effects of the weld line remnant defect on the fracture energy are negligible. For each pin profile and rotational speed, the safe window of traverse speed for defect-free joint production is widened from the conical cylinder pin to the square pin and then to the threaded pin. The ratio of rotational speed to traverse speed (w/v) is the most important parameter in determining almost all characteristics of a joint produced by a specific tool. For each type of pin profile, there is a minimum amount for this ratio, below which the defect formation is very likely to the occurrence. The lowest w/v ratio required to generate a defect-free weld with the conical cylinder pin tool is nearly 2. While that is desirably lowered to 1.6 and 1.25 for square and threaded pin tools, respectively. For all three pin shapes, the hardness value is increased by increasing the traverse speed and decreasing the rotational speed. The grain size and Charpy impact fracture energy values are also reduced by decreasing the rotational speed and increasing the traverse speed. While, a power regression curve (that is very similar to the Hall-Petch relationship) is fitted well to describe the relationship between hardness and grain size, a second-order equation is fitted to the results of fracture energy vs. hardness and grain size. Furthermore, based on the obtained results, it can be concluded that the fracture energy value is more sensitive to the pin profile compared to other outputs of this research (i.e., hardness and grain size). Using the neural network, a relationship was obtained between the input parameters of the process, such as traverse speed, rotational speed, and tool shapes, with the mechanical and microstructural properties of the joints. This relationship was then used in a hybrid multiobjective optimization to find the optimal process parameters. As the best compromise option, hybrid multiobjective optimization proposes a traverse speed of 309 mm/min, a rotational speed of 495 rev/min, and a threaded cylinder pin shape.

An efficient Bi-level hybrid multi-objective reliability-based design optimization of composite structures

This paper presents computationally an efficient multi-objective reliability-based design optimization (RBDO) method for composite structures with uncertain design parameters. The approach adopted is based on a novel hybrid decomposing-based multi-objective particle swarm optimization and sequential quadratic programming algorithms, which is coupled with bi-level modelling and optimization strategy of composite structures and RBDO using Cross entropy method. The reliability analysis considers the rare-event corresponding to the limit states, which is based on Tsai-Wu failure index. In addition, the growing requirements for composite structural design with a minimum weight, introduces significant research challenges in the RBDO approach as the weight and reliability are the two important opposing design requirements. To address this research challenges, the proposed approach involves both reliability and weight as objective functions. A laminated composite plate and benchmark problems were used to demonstrate the efficiency and accuracy of the proposed method. Results show that the proposed method provides computationally efficient and accurate evaluations of the performance of the design of composite structures in comparison to the conventional Monte Carlo simulation method and can provide a potential for solving large-scale multi-objective RBDO of composite structures design problems.

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.

Design of Steam Alternating Solvent Process Operational Parameters Considering Shale Heterogeneity

Summary The steam alternating solvent (SAS) process involves multiple cycles of steam and solvent (e.g., propane) injected into a horizontal well pair to produce heavy oil. These solvent-based methods entail a smaller environmental footprint with reduced water usage and greenhouse gas emissions. However, the lack of understanding regarding the influences of reservoir heterogeneities, such as shale barriers, remains a significant risk for field-scale predictions. Additionally, the proper design of the process is challenging because of the uncertain heterogeneity distribution and optimization of multiple conflicting objectives. This work develops a novel hybrid multiobjective optimization (MOO) workflow to search a set of Pareto-optimal operational parameters for the SAS process in heterogeneous reservoirs. A set of synthetic homogeneous 2D is constructed using data representative of the Cold Lake reservoir. Next, multiple heterogeneous models (realizations) are built to incorporate complex shale heterogeneities. The resultant set of SAS heterogeneous models is subjected to flow simulation. A detailed sensitivity analysis examines the impacts of shale barriers on SAS production. It is used to formulate a set of operational/decision parameters (i.e., solvent concentration and duration of solvent injection cycles) and the objective functions (cumulative steam/oil ratio and propane retention). The nondominated sorting genetic algorithm II (NSGA-II) is applied to search for the optimal decision parameters. Different formulations of an aggregated objective function, including average, minimum, and maximum, are used to capture the variability in objectives among the multiple realizations of the reservoir model. Finally, several proxy models are included in the hybrid workflow to evaluate the defined objective functions to reduce the computational cost. Results of the optimization workflow reveal that both the solvent concentration and duration of the solvent injection in the early cycles have significant impacts. It is recommended to inject solvent for longer periods during both the early and late SAS stages. It is also noted that cases with higher objective function values are observed with more heterogeneities. This work offers promising potential to derisk solvent-based technologies for heavy oil recovery by facilitating more robust field-scale decision-making.