Hybrid Multi-objective Optimization Research Articles

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 CO 2 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. • This work presents a hybrid multi-objective optimization technique (HMO-ACO/FA) based on ant colony optimization (ACO) • 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

• -Effect of pin profile on the grain size, hardness and Charpy impact energy of FSW joint. • -Relationship between hardness, grain size and fracture energy for different welding speeds and pin profiles. • -Multi-objective optimization of output parameters and providing related Pareto front curves. • -Power regression curve (Hall-Petch relationship) is found between hardness and grain size. • -Higher sensitivity of fracture energy to the shape of pin profile compared to grain size and hardness. 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

• A multi-objective multi-response optimization (MRO) model is proposed. • The MRO model addresses location effect, dispersion effect, and model uncertainty. • A multi-objective optimization algorithm is proposed for the MRO model. • The optimization algorithm combines NSGA-II with direct search. • Experiments results verify the effectiveness of the proposed model and algorithm. 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.

A Hybrid Multiobjective Optimization Based on Nondominated Sorting and Crowding Distance, with Applications to Wave Energy Converters

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has attracted a lot of attention since it can handle multiobjective problems (MOP) with a complicated Pareto front. The procedure involves decomposing a MOP into single subproblems, which are eventually optimized simultaneously based on the MOP neighborhood information. However, the MOEA/D strategy tends to produce a distributed optimization that is not of good quality in some problems with complex Pareto optimal front, such as problems with a long tail and sharp peak, common in real-world situations. This paper proposes an improved MOEA/D to enhance the distributed optimization quality and minimize its complexity while accelerating the optimization to get a better solution. The improved method is achieved by incorporating a Hybrid Differential Evolution/Particle Swarm Optimization algorithm and a hybrid operator based on nondominated sorting and crowding distance algorithm. This incorporation takes place in the mutation generator and initial population part of the original MOEA/D algorithm. Simulations and comparisons are carried out based on some MOP benchmark functions to verify the proposed method’s performance. The experimental results show that the proposed method achieves better performance compared to other algorithms. Furthermore, the proposed method is also applied to optimize the multiobjective wave energy converter model to maximize power per year and minimize cost per unit power.

A Hybrid Multi-Objective Particle Swarm Optimization with Central Control Strategy

In recent years, researchers have solved the multi-objective optimization problem by making various improvements to the multi-objective particle swarm optimization algorithm. However, we propose a hybrid multi-objective particle swarm optimization (CCHMOPSO) with a central control strategy. In this algorithm, a disturbance strategy based on boundary fluctuations is first used for the updated new particles and nondominant particles. To prevent the population from falling into a local extremum, some particles are disturbed. Then, when the external archive capacity reaches the extreme value, we use a central control strategy to update the external archive, so that the archive solution gets a good distribution. When the dominance of the current particle and the individual best particle cannot be determined, to enhance the diversity of the population, the combination method of the current particle and the individual best particle can be used to update the individual best particle. The experimental results show that CCHMOPSO is better than four multi-objective particle swarm optimization algorithms and four multi-objective evolutionary algorithms. It is a feasible method for solving multi-objective optimization problems.

A Multi-objective Hybrid Optimization for renewable energy integrated Electrical Power Transmission Expansion Planning

Due to the large size of conventional electrical power transmission systems and the large number of uncertainties involved, achieving the most favourable Transmission Expansion Planning solution turns out to be almost impossible. The proposed method intends to develop a novel method to solve Transmission Expansion Planning problems in electric power systems incorporating renewable energy sources like wind turbines and Photo Voltaic array using IEEE 24 Reliability Test System. For enhancing the efficiency of search processes and to make its use easier on diverse networks and operations, the hybridization of two renowned meta-heuristic algorithms known as Grey Wolf Optimization (GWO) and Genetic Algorithm (GA) termed as Grey Wolf with Genetic Algorithm (GWGA) is adopted. A novel distance factor based on the best position and current position of the solution in Grey wolf optimization is introduced and proposed for the hybridization technique and gives a quick and promising solution with reduced computational time. The GWO and GA algorithms are combined suitably to achieve the advantages of both algorithms. With this proposed model, the investment cost of the transmission line and the maximum amount of power that can be distributed to the consumer is optimized with an objective of minimum load shedding. Among the state-of-the-art optimization techniques considered, a remarkable performance percentage improvement in the expansion plan in terms of cost reduction and load shedding minimization has been obtained in GWO, but when hybridized with GA, an improvement of 13.42% in cost function and 18.65% in load shedding is achieved for a population size of 60. Hence, the proposed method guarantees to generate the best solution with a faster convergence resulting in reduced computational time.

Comparative analysis of DoE-based hybrid multiobjective optimization techniques for inclined laser drilling of glass fiber reinforced plastics

Comparative analysis of DoE-based hybrid multiobjective optimization techniques for inclined laser drilling of glass fiber reinforced plastics

Hybrid multi-objective optimization approach intended for mobile robot path planning model

In the field of mobile robot decision making and control, path planning is an essential element as it defines the performance of the design. It is one of the hot topics in artificial intelligence and researchers pay more attention to develop an efficient model. The key requirements that must be considered while designing a navigational system for mobile robots are origin point, obstacles, destination point, path planning, and realistic decision mechanism. However, conventional systems have limitations as slow response, long planning, large turns, and unsafe factors. Aiming at the problems, this research work presents a hybrid optimized path planning model for a mobile robot. Improved particle swarm optimization and Modified Whale optimization models are incorporated as a hybrid multi-objective approach to obtain the shortest, smoothest, and safest path for a mobile robot. Experimental results demonstrate that the proposed hybrid optimization model is suitable for mobile robot navigation for dynamic environments by obtaining a shorter, smoother, and safer path than existing algorithms.

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.