metaheuristic algorithms

This paper introduces the Greedy Man Optimization Algorithm (GMOA), a novel bio-inspired metaheuristic approach for solving complex optimization problems. Inspired by competitive individuals resisting change, GMOA incorporates two unique mechanisms: MMO resistance, which prevents premature replacement of solutions, and periodic parasite removal, which promotes diversity and avoids stagnation. The algorithm is evaluated on standard benchmark functions, including Sphere, Rastrigin, Rosenbrock, and Griewank, and its performance is compared with established algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Ant Colony Optimization (ACO). Results demonstrate that GMOA outperforms these methods in terms of solution quality, convergence rate, and robustness. Statistical significance tests validate the reliability of the results. GMOA’s ability to balance exploration and exploitation makes it a promising tool for various real-world applications, including supply chain optimization and healthcare resource allocation.

Sudden and severe stock price crashes pose a significant challenge to stock markets. The substantial losses incurred from such events underscore the need for more effective forecasting tools. This study aims to enhance the predictive power of models for stock price crashes in Tehran Stock Exchange and commenced with a comprehensive literature review to identify key financial factors influencing stock price volatility. Given the high dimensionality of the dataset and the extended time period, metaheuristic algorithms were employed for feature selection. 10 algorithms, namely Ant Colony Optimization, Hill Climbing, Las Vegas, Whale Optimization, Simulated Annealing, Genetic Algorithm, Tabu Search, Particle Swarm Optimization (PSO), Honey Bee (HBA) and Firefly were utilized to reduce dimensionality and enhance model performance. Subsequently, Support Vector Machines were implemented to develop predictive models. The models were trained and evaluated using historical data from Tehran Stock Exchange spanning from 2001 to 2020. The findings of this research demonstrate that combining metaheuristic algorithms for model reduction and optimization, along with advanced machine learning techniques, yields results that can significantly improve investment decision-making.

Nowadays, the project scheduling problem with limited resources has become one of the most important optimization issues. Given the enormous costs spent on projects as well as materials, resources, and forces, projects are expected to be successful and finish at the planned time. This schedule in functional mode will have many uncertainties due to the needs of the moment. Such a goal requires careful planning and advanced algorithms to solve these complex issues in a short and reasonable time. In this research, a new method is presented using the Intelligent Water Drops (IWD) algorithm for the resource-constrained project scheduling problem. For this reason and because of the importance of these projects, in this research, an optimization model has been developed for project scheduling in the state of uncertainty, which can solve many implementation obstacles. For this purpose, first, the problem is formulated as a mixed-inter linear programming (MILP) model. Next, the model is optimized using the IDW algorithm. To evaluate the performance of the proposed method, the standard data set was used in previous research and articles, and four datasets with different scales were selected from the PSLIB library. The results show that the proposed method is capable of obtaining the best precision in terms of the least critical deviation from the optimal solution. Moreover, the results of the proposed method were compared with metaheuristic algorithms, such as the particle congestion algorithm (PSO) , which was able to get the best solution among these algorithms.

Great attention should be paid to planning and scheduling surgeries in the operating room which is the most sensitive ward in the health context in terms of cost and specific sensitivity due to its association with the life and death of individuals. In this case, reusable sterile equipment and devices are crucial issues because the hospital or nosocomial infections result from insufficient sterilization of these instruments. Therefore, sterilization of reusable medical devices is a necessity in the operating room to prevent possible infections. This study solves the integrated operating rooms and sterile section planning problem to minimize the total costs of sterilization, surgery postponement, and performance. This study also minimizes the completion time of surgery considering nondeterministic operating times and emergency-elective patients. In the real world, surgery time may be nondeterministic based on the conditions of the patient, surgeon, equipment, and instruments; hence, it is valuable to find a robust solution for planning under such circumstances. After presenting a bi-objective mathematical model for this problem, an improved epsilon constraint method was used to solve problems with small dimensions, and two metaheuristics NSGA-II and NRGA were developed for large dimensions regarding NP-hard problems. These two algorithms were analysed in terms of five indicators. The results indicated the superiority of the NSGA-II algorithm over NRGA to solve such problems.

A novel mixed integer non-linear mathematical model is presented in this paper for the two-echelon allocation-routing problem by applying the conditions of the route and transportation fleet under uncertainty. The cost of allocating drivers to non-homogeneous vehicles is calculated in this model based on the type of the vehicle, the lifecycle of the car, the experience of the driver, and different degrees of hardness that are defined for various routes. The cost of passing the route is defined based on an initial fixed cost and the degree of hardness of the route. Also, the reliability of the routes in each section is defined as an objective in the second echelon of the model aimed at enhancing the reliability rate. Two metaheuristic algorithms, NSGAII and MOPSO, are utilized to solve the model. Then, their performance rates in problems with different sizes are statistically evaluated and compared by different indices, following the adjustment of their parameters by Taguchi's method, through which results indicated the high efficiency of the model. A sensitivity analysis is ultimately performed on the results obtained from the solution, and some suggestions are made for the development of the model.

Over the past decade, solving complex optimization problems with metaheuristic algorithms has attracted many experts and researchers.There are exact methods and approximate methods to solve optimization problems. Nature has always been a model for humans to draw the best mechanisms and the best engineering out of it and use it to solve their problems. The concept of optimization is evident in several natural processes, such as the evolution of species, the behavior of social groups, the immune system, and the search strategies of various animal populations. For this purpose, the use of nature-inspired optimization algorithms is increasingly being developed to solve various scientific and engineering problems due to their simplicity and flexibility. Anything in a particular situation can solve a significant problem for human society. This paper presents a comprehensive overview of the metaheuristic algorithms and classifications in this field and offers a novel classification based on the features of these algorithms.

In this study a scenario-based multi-objective fuzzy model was provided in the SCLSC , which in addition to three aspects of sustainability including, social impact such as the creation of job opportunities, customer satisfaction, and so on, environmental impact such as reducing air pollution, and so on, economic impact such as reducing cost, increasing the reliability of the SC and product routing have been modeled. Two algorithms, including MOPSO and NSGA-II Algorithms, were applied to solve the proposed model. After tuning their parameters by the Taguchi method, their performance in problems with different dimensions were tested followed by evaluating them by powerful criteria. The proposed model was implemented on Chipboard Pooya Company in Iran in two scenarios of economic recession and prosperity aimed at evaluating its accuracy. A sensitivity analysis was eventually performed on the proposed model followed by making some suggestions to develop the model.

Cryptocurrencies are considered as new financial and economic tools having special and innovative features, among which Bitcoin is the most popular. The contribution of the Bitcoin market continues to grow due to the special nature of Bitcoin. The investors' attention to Bitcoin has increased significantly in recent years due to significant growth in its prices. It is important to create a prediction system which works well for investment management and business strategies due to the high chaos and volatility of Bitcoin prices. In this study, in order to improve predictive accuracy, Bitcoin price dataset is first divided into a time interval through time window, then propose a new model based on Long Short-Term Memory (LSTM) neural networks and Metaheuristic algorithms. Chaotic Dolphin Swarm Optimization algorithm is used to optimize the LSTM. Performance evaluation indicated that the proposed model can have more effective predictions and improve prediction accuracy. In addition, the performance of the optimized model is better and more reliable than other models.

This study considers outsourcing decisions in a flexible flow shop scheduling problem, in which each job can be processed by either an in-house production line or outsourced. The selected objective function aims to minimize the weighted sum of tardiness costs, in-house production costs, and outsourcing costs with respect to the jobs due date. The purpose of the problem is to select the jobs that must be processed in-house, schedule processing of the jobs in-house, and finally select and assign other jobs to the subcontractors. We develop a mixed-integer linear programming (MILP) model for the research problem. Regarding the complexity of the research problem, the MILP model cannot be used for large-scale problems. Therefore, four metaheuristic algorithms, including SA, GA, PSO, hybrid PSO-SA, are proposed to solve the problem. Furthermore, some random test problems with different sizes are generated to evaluate the effectiveness of the proposed MILP model and solution approaches. The obtained results demonstrate that the GA can obtain better solutions in comparison to the other algorithms.

Because of the high costs for the delivery, manufacturers are generally needed to dispatch their products in a batch delivery system. However, using such a system leads to some adverse effects, such as increasing the number of tardy jobs. The current paper investigates the two-machine flow-shop scheduling problem where jobs are processed in series on two stages and then dispatched to customers in batches. The objective is to minimize the batch delivery cost and tardiness cost related to the number of tardy jobs. First, a mixed-integer linear programming model (MILP) is proposed to explain this problem. Because the problem under consideration is NP-hard, the MILP model cannot solve large-size instances in a reasonable running time. Some metaheuristic algorithms are provided to solve the large-size instances, including BA, PSO, GA, and a novel Hybrid Bees Algorithm (HBA). Using Friedman and Wilcoxon signed-ranks tests, these intelligent algorithms are compared, and the results are analyzed. The results indicate that the HBA provides the best performance for large-size problems.

Non-uniform distribution of customers in a region and variation of their maximum willingness to pay at distinct areas make regional pricing a practical method to maximize the profit of the distribution system. By subtracting the classic objective function, which minimizes operational costs from revenue function, profit maximization is aimed. A distribution network is designed by determining the number of trucks to each established distribution center, allocating customers in routes, and inventory levels of customers. Also, environmental impacts, including fuel consumption and CO2 emission, aimed to be minimized. So, a new quadratic mixed-integer programming model is presented for the Green Transportation Location-Inventory-Routing Problem integrated with dynamic regional pricing problem (GTLIRP+DRP). The model is applied to the real case study, to show its competent application. To tackle this problem, a Hybrid Bees Algorithm (HBA) is developed and verified by the genetic algorithm. Finally, managers suggested using HBA that achieves better solutions in the less computational time.

Planning the freight flow from the plants to the customer zones is one of the most challenging problems in the field of supply chain management. Because of many traffic regulations, oversize/overweight vehicles often are not permitted to enter city boundaries. Therefore, intermediate facilities (city distribution centers) play a very important role in distribution networks. Accordingly, in this paper, transportation of goods from the plants to the customers is considered an integrated process containing two phases, namely, transportation from plant to distribution centers and distribution from city distribution centers to customers using small and environmentally-friendly vehicles. The Transportation Location Routing Problem (TLRP) studied can be considered as an extension of the two-echelon location routing problem. Minimizing the operational costs, and the workload balancing of the heterogeneous fleet in the distribution phase are considered as the two objective functions. A Mixed Integer Programming (MIP) model, as well as two solution approaches, based on Multi-objective Particle Swarm Optimization Algorithm, and Non-dominated Sorting Genetic Algorithm, is presented for the problem. In order to illustrate the efficacy of the proposed methods, they have been implemented on test problems of different sizes. The results show the methods are able to produce efficient solutions in a reasonable amount of time.

Sustainability is a monumental issue that should be considered in designing a logistics system. In order to incorporate sustainability concepts in our study, a waste collection problem with economic, environmental, and social objective functions was addressed. The first objective function minimized overall costs of the system, including establishment of depots and treatment facilities. Addressing environmental concerns, greenhouse gases emission was minimized by the second objective function and the third one maximized distances between each customer and treatment facilities. Treatment facility is noxious for human health and should be located in the maximum distance from the urban area. Initially, the locations of depots and treatment facilities were determined. Then, heterogeneous vehicles started to collect waste from the location of each customer and take it to treatment facilities. The problem included two types of open and close routes. Moreover, each vehicle had a capacity restriction, servicing time, and route length. There were different types of waste and each vehicle had a different capacity for them. Three metaheuristic algorithms combined with clustering approach were proposed to look for the best solutions in rational time. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II), improved Strength Pareto Evolutionary Algorithm (SPEA-II), and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) were compared in terms of performance metrics. According to the results, NSGA-II outweighed other algorithms in the presented model.

This paper studies the ambulance routing problem (ARP) in disaster situations when a large number of injured people from various locations require receiving treatments and medical aids. In such circumstances, many people summoning the ambulances but the capacity and number of emergency vehicles are not sufficient to visit all the patients at the same time. Therefore, a pivotal issue is to manage the fleet of ambulances to meet all the requests promptly and consequently mitigate human suffering. We considered three different categories of patients with various requirements. Moreover, the support ambulances are segmented into various classes based on their capabilities. A mathematical formulation is presented to obtain route plans with the aim of minimizing the latest service completion time among the patients. Since the patient’s condition gets worse and becomes life threatening over the time, semi-soft time window constraint is incorporated to reflect the penalties on late arrivals using survival function. Since the presented model belongs to the class of NP-hard problems, two efficient meta-heuristic algorithms based on genetic algorithm and tabu search are proposed to cope with real size problems. The experiments show that the proposed model could present proper routes and adopt the types of ambulances with the patients’ needs to increase the service quality. Moreover, the proposed metaheuristics are capable to find acceptable solutions for the problem in reasonable computational times.

The fixed charge transportation problem (FCTP) is a deployment of the classical transportation problem in which a fixed cost is incurred, independent of the amount transported, along with a variable cost that is proportional to the amount shipped. Since the problem is considered as an NP-hard, the computational time grows exponentially as the size of the problem increases. In this paper, we propose a new heuristic along with well-known metaheuristic like Geneticalgorithm (GA), simulated annealing (SA) and recently developed one, Keshtel algorithm (KA) to solve the FCTP. Contrary to previous works, we develop a simple and strong heuristic according to the nature of the problem and compare the result with metaheuristics. In addition, since the researchers recently used the priority-based representation to encode the transportation graphs and achieved very good results, we consider this representation in metaheuristics and compare the results with the proposed heuristic. Furthermore, we apply the Taguchi experimental design method to set the proper values of algorithms in order to improve their performances. Finally, computational results of heuristic and metaheuristics with different encoding approaches, both in terms of the solution quality and computation time, are studied in different problem sizes.

Multi-criteria sequence dependent setup times scheduling problems exist almost everywhere in real modern manufacturing world environments. Among them, Sequence Dependent Setup Times-Multi-Objective Hybrid Flowshop Scheduling Problem (SDST-MOHFSP) has been an intensifying attention of researchers and practitioners in the last three decades. In this paper, we briefly summarized and classified the current standing of SDST-MOHFSP. All publications are categorized regarding the solution methods, as well as the structure of the hybrid flowshop which helps researcher and practitioner to use/modify proper solution algorithm for solving their specific problem. Furthermore, based on the review of the existing papers, the need for future research is recognized. Accordingly, by recognizing the research gaps, a large number of recommendations for further study have been proposed.