Multi-objective Invasive Weed Optimization Research Articles

Dual resource constrained flexible job shop scheduling with sequence‐dependent setup time

AbstractThis study addresses the imperative need for efficient solutions in the context of the dual resource constrained flexible job shop scheduling problem with sequence‐dependent setup times (DRCFJS‐SDSTs). We introduce a pioneering tri‐objective mixed‐integer linear mathematical model tailored to this complex challenge. Our model is designed to optimize the assignment of operations to candidate multi‐skilled machines and operators, with the primary goals of minimizing operators' idleness cost and sequence‐dependent setup time‐related expenses. Additionally, it aims to mitigate total tardiness and earliness penalties while regulating maximum machine workload. Given the NP‐hard nature of the proposed DRCFJS‐SDST, we employ the epsilon constraint method to derive exact optimal solutions for small‐scale problems. For larger instances, we develop a modified variant of the multi‐objective invasive weed optimization (MOIWO) algorithm, enhanced by a fuzzy sorting algorithm for competitive exclusion. In the absence of established benchmarks in the literature, we validate our solutions against those generated by multi‐objective particle swarm optimization (MOPSO) and non‐dominated sorted genetic algorithm (NSGA‐II). Through comparative analysis, we demonstrate the superior performance of MOIWO. Specifically, when compared with NSGA‐II, MOIWO achieves success rates of 90.83% and shows similar performance in 4.17% of cases. Moreover, compared with MOPSO, MOIWO achieves success rates of 84.17% and exhibits similar performance in 9.17% of cases. These findings contribute significantly to the advancement of scheduling optimization methodologies.

Self-adaptive metaheuristic optimization technique for multi-objective reservoir operation

Abstract Multi-objective reservoir operation presents a number of critical challenges that must be overcome for efficient management of water resources. The inherent contradiction between several goals, such as satisfying irrigation demand and maximizing hydropower generation, is one of the major issues. Trade-offs and compromises must be carefully considered to balance these objectives. To solve this problem, a study was carried out to optimize the operation of multi-objective reservoirs with two primary goals: minimizing irrigation deficits and maximizing hydropower generation. This study employs the self-adaptive multipopulation multi-objective Jaya algorithm (SAMP-MOJA), an improved version of the Jaya algorithm, to construct an optimal Pareto Front utilizing an a priori approach. The performance of SAMP-MOJA is compared to that of other algorithms such as multi-objective particle swarm optimization, multi-objective invasive weed optimization, and multi-objective Jaya algorithm. The results of this study demonstrate that the hydropower generated by the developed model surpasses 80% of the actual generation. The study's findings will aid in designing the most effective Pareto front possible.

Development of the Multi-Objective Invasive Weed Optimization Algorithm in the Integrated Water Resources Allocation Problem

Development of the Multi-Objective Invasive Weed Optimization Algorithm in the Integrated Water Resources Allocation Problem

Designing a sustainable bioethanol supply chain network: A combination of machine learning and meta-heuristic algorithms

Bioethanol demands have increased during the last decade due to unexpected events worldwide. It is among the renewable energy sources that are utilized to replace fossil-fuel-based energy. Designing an efficient, Sustainable Bioethanol Supply Chain Network (SBSCN) is a critical task for the government and communities to manage the bioethanol demands appropriately. The contribution of this study is threefold. First, this study project the demand using three popular machine learning methods, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Ensemble Learning (Adaboost) methods, to find the best one to be utilized as an input to the proposed mathematical to find optimal values for strategic, planning, and tactical decision variables. Second, the unemployment rate is considered an important parameter of the model to maximize the social effects. Third, since the proposed model of this study is NP-hard, to solve the problem, the CPLEX solver is applied for small size and two meta-heuristic algorithms, including Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Invasive Weed Optimization (MOIWO) are considered for medium and large size problems to find Pareto optimal solutions. Due to the sensitivity of two meta-heuristics algorithms, the Taguchi method, such as Max Spread (MS) and Mean Ideal Distance (MID), is utilized to control the parameters of those two applied algorithms. Since North Dakota (ND) is among the states with the most potential to produce bioethanol due to its vast land area, including marginal cropland and Conservation Research Program (CRP), the proposed model has been evaluated and validated based on the ND case study. The results show that the MOIWO algorithm outperforms NSGA-II based on the proposed model and the case study of this paper. Also, this algorithm is more reliable in terms of solution quality to tackle the problem. Finally, some research directions are discussed for future studies, and managerial insights are provided. • Bioethanol consumption experienced an increasing due to unexpected worldwide event. • Utilizing machine learning approaches to estimate the bioethanol demand in N.D. • Designing sustainable bioethanol supply chain by two meta-heuristic algorithms. • Optimal locations of biorefinery, pre-processing, distribution, and demand centers. • Gradual replacement of liquid fossil fuels with bioethanol to reduce air pollutions.

A robust fuzzy multi-objective location-routing problem for hazardous waste under uncertain conditions.

Industrialization and population growth have been accompanied by many problems such as waste management worldwide. Waste management and reduction have a vital role in national management. The presents study represents a multi-objective location-routing problem for hazardous wastes. The model was solved using Non dominated Sorting Genetic Algorithm-II, Multi-Objective Particle Swarm Optimization, Multi-Objective Invasive Weed Optimization, Pareto Envelope-based Selection Algorithm, Multi-Objective Evolutionary Algorithm Based on Decomposition and Multi-Objective Grey Wolf Optimizer algorithms. The findings revealed that the Multi-Objective Invasive Weed Optimization algorithm was the best and the most efficient among the algorithms used in this study. Obtaining income from the incineration of the wastes and reducing the risk of COVID-19 infection are the first innovation of the present study, which considered in the presented model. The second innovation is that uncertainty was considered for some of the crucial parameters of the model while the robust fuzzy optimization model was applied. Besides, the model was solved using several meta-heuristic algorithms such as Multi-Objective Invasive Weed Optimization, Multi-Objective Evolutionary Algorithm Based on Decomposition and Multi-Objective Grey Wolf Optimizer, which were rarely used in literature. Eventually, the most efficient algorithm was identified by comparing the considered 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.

Green cross-dock based supply chain network design under demand uncertainty using new metaheuristic algorithms

<p style='text-indent:20px;'>This study concerns the optimization of green supply chain network design under demand uncertainty. The issue of demand uncertainty has been addressed using a scenario-based analysis approach. The main contribution of this research is to investigate the optimization of cross-dock based supply chain under uncertainty using scenario-based formulation and metaheuristic algorithms. The problem has been formulated as a two-objective mathematical model with the objectives of minimizing the costs and minimizing the environmental impact of the supply chain. Two metaheuristic algorithms, namely non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective invasive weed optimization (MOIWO), have been developed to optimize this mathematical model. This paper focuses on the use of new metaheuristic algorithms such as MOIWO in green supply chain network design, which has received less attention in previous studies. The performance of the two solution methods has been evaluated in terms of three indices, which measure the quality, spacing, and diversification of solutions. Evaluations indicate that the developed MOIWO algorithm produces more Pareto solutions and solutions of higher quality than NSGA-II. A performance test carried out with 31 problem instances of different sizes shows that the two methods perform similarly in terms of the spread of solutions on the Pareto front, but MOIWO provides higher quality solutions than NSGA-II.</p>

A stochastic bi-objective simulation–optimization model for plasma supply chain in case of COVID-19 outbreak

As of March 24, 2020, the Food and Drug Administration (FDA) authorized to bleed the newly recovered from Coronavirus Disease 2019 (COVID-19), i.e., the ones whose lives were at risk, separate Plasma from their blood and inject it to COVID-19 patients. In many cases, as observed the plasma antibodies have cured the disease. Therefore, a four-echelon supply chain has been designed in this study to locate the blood collection centers, to find out how the collection centers are allocated to the temporary or permanent plasma-processing facilities, how the temporary facilities are allocated to the permanent ones, along with determining the allocation of the temporary and permanent facilities to hospitals. A simulation approach has been employed to investigate the structure of COVID-19 outbreak and to simulate the quantity of plasma demand. The proposed bi-objective model has been solved in small and medium scales using ɛ-constraint method, Strength Pareto Evolutionary Algorithm II (SPEA-II), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi Objective Invasive Weed Optimization algorithm (MOIWO) approaches. One of the novelties of this research is to study the system dynamic structure of COVID-19’s prevalence so that to estimate the required plasma level by simulation. Besides, this paper has focused on blood substitutability which is becoming increasingly important for timely access to blood. Due to shorter computational time and higher solution quality, MOIWO is selected to solve the proposed model for a large-scale case study in Iran. The achieved results indicated that as the plasma demand increases, the amount of total system costs and flow time rise, too. The proposed simulation model has also been able to calculate the required plasma demand with 95% confidence interval.

Problem-specific multi-objective invasive weed optimization algorithm for reconnaissance mission scheduling problem

With the progress of technology, the multi-agent system is successfully applied in many applications. In this paper, we investigate the problem of multi-agent system reconnaissance mission scheduling, which is the core of the reconnaissance decision support system and can be modeled as an extension of Multi-Mode Multi-Skill Resource-Constrained Project Scheduling Problem. Three objectives are considered in this paper: (1) minimizing the reconnaissance mission’s makespan, (2) minimizing the total cost of allocating reconnaissance agents, and (3) maximizing the total quality of all reconnaissance tasks. An effective problem-specific multi-objective invasive weed optimization algorithm (PS-MOIWO) is proposed for solving the problem. Firstly, a new chromosome structure guaranteeing the feasibility of solutions and an initialization method are proposed. Secondly, we propose a self-adaptive penalty-based constraint handling technique to describe the fitness of each individual and adopt a novel non-dominated sorting method to rank the population. Thirdly, by using the problem-specific knowledge, a local search procedure is developed and incorporated into the PS-MOIWO framework to enhance the exploitation ability. Based on the Taguchi method, algorithm’s suitable parameter combinations are determined. Simulation results based on a set of newly generated reconnaissance instances and the comparisons with some existing algorithms demonstrate the proposed algorithm’s effectiveness.

A multi-objective vibration damping optimization algorithm for solving a cellular manufacturing system with manpower and tool allocation

In this paper,a novel bi-objectivemathematical model is proposed to designa four-dimensional (i.e.,part, machine, operator, and tool) cellular manufacturing system (CMS) in a dynamic environment. The main objectives of this model are to 1) minimize total costs including tools processing cost, costs of transporting cells between various cells, machine setup cost, and operators’ educational costs, and 2) maximizing skill level of operators. The developedmodel is strictly NP-hard and exact algorithms cannot find globally optimal solutions in reasonably computational time. So, a multi-objective vibration damping optimization algorithm (MOVDO) with a new solution structure that satisfies all the constraints and generates feasible solutions is proposed to find near-optimal solutions in reasonablycomputational time. Since there is no benchmark available inthe literature, three other meta-heuristic algorithms (i.e., non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO) and multi-objective invasive weeds optimization (MOIWO)) with the similar solution structure are developed to validate theperformance of the proposedMOVDOalgorithm for solving various instances of the developed model. A Taguchi method is employed to calibrate the main parameters ofthese fouralgorithms. The result of comparing theirperformances based on statistical tests and different measuring metrics reveals that theproposed MOVDO algorithm outperforms remarkably better than other meta-heuristics used in this paper.

Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm

The reliability of machinery and automated guided vehicle has been one of the most important challenges to enhance production efficiency in several manufacturing systems. Reliability improvement would result in a simultaneous reduction of both production times and transportation costs of the materials, especially in automated guided vehicles. This article aims to conduct a practical multi-objective reliability optimization model for both automated guided vehicles and the machinery involved in a job-shop manufacturing system, where different machines and the storage area through some parallel automated guided vehicles handle materials, parts, and other production needs. While similar machines in each shop are limited to failures based on either an Exponential or a Weibull distribution via a constant rate, the machines in different shops fail based on different failure rates. Meanwhile, as the model does not contain any closed-form equation to measure the machine reliability in the case of Weibull failure, a simulation approach is employed to estimate the shop reliability to be further maximized using the proposed model. Besides, the automated guided vehicles are restricted to failures according to an Exponential distribution. Furthermore, choosing the best locations of the shops is proposed among some potential places. The proposed NP-Hard problem is then solved by designing a novel non-dominated sorting cuckoo search algorithm. Furthermore, a multi-objective teaching-learning-based optimization, as well as a multi-objective invasive weed optimization are designed to validate the results obtained. Ultimately, a novel AHP-TOPSIS method is carried out to rank the algorithms in terms of six performance metrics.

Big Data-Driven Cognitive Computing System for Optimization of Social Media Analytics

The integration of big data analytics and cognitive computing results in a new model that can provide the utilization of the most complicated advances in industry and its relevant decision-making processes as well as resolving failures faced during big data analytics. In E-projects portfolio selection (EPPS) problem, big data-driven decision-making has a great importance in web development environments. EPPS problem deals with choosing a set of the best investment projects on social media such that maximum return with minimum risk is achieved. To optimize the EPPS problem on social media, this study aims to develop a hybrid fuzzy multi-objective optimization algorithm, named as NSGA-III-MOIWO encompassing the non-dominated sorting genetic algorithm III (NSGA-III) and multi-objective invasive weed optimization (MOIWO) algorithms. The objectives are to simultaneously minimize variance, skewness and kurtosis as the risk measures and maximize the total expected return. To evaluate the performance of the proposed hybrid algorithm, the data derived from 125 active E-projects in an Iranian web development company are analyzed and employed over the period 2014-2018. Finally, the obtained experimental results provide the optimal policy based on the main limitations of the system and it is demonstrated that the NSGA-III-MOIWO outperforms the NSGA-III and MOIWO in finding efficient investment boundaries in EPPS problems. Finally, an efficient statistical-comparative analysis is performed to test the performance of NSGA-III-MOIWO against some well-known multi-objective algorithms.

A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images

A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images

A robust bi-objective multi-trip periodic capacitated arc routing problem for urban waste collection using a multi-objective invasive weed optimization.

Urban waste collection is one of the principal processes in municipalities with large expenses and laborious operations. Among the important issues raised in this regard, the lack of awareness of the exact amount of generated waste makes difficulties in the processes of collection, transportation and disposal. To this end, investigating the waste collection issue under uncertainty can play a key role in the decision-making process of managers. This paper addresses a novel robust bi-objective multi-trip periodic capacitated arc routing problem under demand uncertainty to treat the urban waste collection problem. The objectives are to minimize the total cost (i.e. traversing and vehicles' usage costs) and minimize the longest tour distance of vehicles (makespan). To validate the proposed bi-objective robust model, the ε-constraint method is implemented using the CPLEX solver of GAMS software. Furthermore, a multi-objective invasive weed optimization algorithm is then developed to solve the problem in real-world sizes. The parameters of the multi-objective invasive weed optimization are tuned optimally using the Taguchi design method to enhance its performance. The computational results conducted on different test problems demonstrate that the proposed algorithm can generate high-quality solutions considering three indexes of mean of ideal distance, number of solutions and central processing unit time. It is proved that the ε-constraint method and multi-objective invasive weed optimization can efficiently solve the small- and large-sized problems, respectively. Finally, a sensitivity analysis is performed on one of the main parameters of the problem to study the behavior of the objective functions and provide the optimal policy.

Decomposition-Based Multiobjective Optimization with Invasive Weed Colonies

In order to solve the multiobjective optimization problems efficiently, this paper presents a hybrid multiobjective optimization algorithm which originates from invasive weed optimization (IWO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), a popular framework for multiobjective optimization. IWO is a simple but powerful numerical stochastic optimization method inspired from colonizing weeds; it is very robust and well adapted to changes in the environment. Based on the smart and distinct features of IWO and MOEA/D, we introduce multiobjective invasive weed optimization algorithm based on decomposition, abbreviated as MOEA/D-IWO, and try to combine their excellent features in this hybrid algorithm. The efficiency of the algorithm both in convergence speed and optimality of results are compared with MOEA/D and some other popular multiobjective optimization algorithms through a big set of experiments on benchmark functions. Experimental results show the competitive performance of MOEA/D-IWO in solving these complicated multiobjective optimization problems.

Multiobjective fuzzy mathematical model for a financially constrained closed‐loop supply chain with labor employment

AbstractThis paper addresses the multiobjective, multiproducts and multiperiod closed‐loop supply chain network design with uncertain parameters, whose aim is to incorporate the financial flow as the cash flow and debts' constraints and labor employment under fuzzy uncertainty. The objectives of the proposed mathematical model are to maximize the increase in cash flow, maximize the total created jobs in the supply chain, and maximize the reliability of consumed raw materials. To encounter the fuzzy uncertainty in this model, a possibilistic programming approach is used. To solve large‐sized problems, the multiobjective simulated annealing algorithm, multiobjective gray wolf optimization, and multiobjective invasive weed optimization are proposed and developed. The numerical results demonstrate that these algorithms solve the problems within about 1% of the required solving time for the augmented ε‐constraint and have similar performance and even better in some cases. The multiobjective simulated annealing algorithm with a weak performance takes less time than the other two algorithms. The multiobjective gray wolf optimization and multiobjective invasive weed optimization algorithms are superior based on the multiobjective performance indices.

Multi-objective teaching-learning-based meta-heuristic algorithms to solve multi-skilled project scheduling problem

In this paper, a novel multi-objective multi-skilled resource-constrained project scheduling problem based on learning effect is presented in which efficiency of the workforce is proportional with the time they spend to execute that skill. A mixed integer nonlinear programming formulation is presented to optimize conflicting objectives. Two hybrid multi-objective teaching–learning based optimization (TLBO) algorithms are developed with a new solution structure based on primary concepts of multi-objective particle swarm optimization and multi-objective invasive weeds optimization algorithms to find near optimal solutions of the model’s complicated instances in a reasonable computational time. The proposed algorithms encompass a novel solution structure that satisfies all the constraints and generates feasible solutions. The classical multi-objective teaching-learning based optimization algorithm and NSGA II are developed with the same chromosome structure to validate performance of the developed algorithms. In addition, all the algorithms are employed to solve small- and large-scale benchmark instances that are available on literature of project scheduling problems as well as 30 randomly generated test problems. Taguchi grey relational analysis is used to tune parameters of the algorithms and enhance their performance for generating more qualified solutions. In addition, statistical test is employed to compare performance of the proposed algorithms in terms of some multi-objective metrics. Finally, hybrid multi-attribute decision making approach is employed to prioritize algorithms and determine the algorithm with higher performance on solving small, medium and large scale instances.

Solving Multi-objective Optimal Control Problems of chemical processes using Hybrid Evolutionary Algorithm

Evolutionary algorithms have been recognized to be suitable for extracting approximate solutions of multi-objective problems because of their capability to evolve a set of non-dominated solutions distributed along the Pareto frontier‎. ‎This paper applies an evolutionary optimization scheme‎, ‎inspired by Multi-objective Invasive Weed Optimization (MOIWO) and Non-dominated Sorting (NS) strategies‎, ‎to find approximate solutions for multi-objective optimal control problems (MOCPs)‎. ‎The desired control function may be subjected to severe changes over a period of time‎. ‎In response to deficiency‎, ‎the process of dispersal has been modified in the MOIWO‎. ‎This modification will increase the exploration power of the weeds and reduces the search space gradually during the iteration process‎. ‎ ‎The performance of the proposed algorithm ‎is compared with conventional Non-dominated Sorting Genetic Algorithm (NSGA-II) and Non-dominated Sorting Invasive Weed Optimization (NSIWO) algorithm‎.The results show that the proposed algorithm has better performance than others in terms of computing time‎, ‎convergence rate and diversity of solutions on the Pareto ‎frontier.

A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand

This paper addresses a robust multi-objective multi-period aggregate production planning (APP) problem based on different scenarios under uncertain seasonal demand. The main goals are to minimize the total cost including in-house production, outsourcing, workforce, holding, shortage and employment/unemployment costs, and maximize the customers’ satisfaction level. To deal with demand uncertainty, robust optimization approach is applied to the proposed mixed integer linear programming model. A goal programming method is then implemented to cope with the multi-objectiveness and validate the suggested robust model. Since APP problems are classified as NP-hard, two solution methods of non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective invasive weed optimization algorithm (MOIWO) are designed to solve the problem. Moreover, Taguchi design method is implemented to increase the efficiency of the algorithms by adjusting the algorithms’ parameters optimally. Finally, several numerical test problems are generated in different sizes to evaluate the performance of the algorithms. The results obtained from different comparison criteria demonstrate the high quality of the proposed solution methods in terms of speed and accuracy in finding optimal solutions.

A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images

A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images