meta-heuristic algorithms

The Traveling Salesman Problem (TSP) is a well-known problem in optimization and graph theory, where finding the optimal solution has always been of significant interest. Optimal solutions to TSP can help reduce costs and increase efficiency across various fields. Heuristic algorithms are often employed to solve TSP, as they are more efficient than exact methods due to the complexity and large search space of the problem. In this study, meta-heuristic algorithms such as the Genetic Algorithm and the Teaching-Learning Based Optimization (TLBO) algorithm are used to solve the TSP. Additionally, a discrete mutation phase is introduced to the TLBO algorithm to enhance its performance in solving the TSP. The results indicate that, in testing two specific models of the TSP, the modified TLBO algorithm outperforms both the Genetic Algorithm and the standard TLBO algorithm in terms of convergence to the optimal solution and response time.

In the field of optimization, metaheuristic algorithms have garnered significant interest. These algorithms, which draw inspiration from natural selection, evolution, and problem-solving strategies, offer an alternative approach to solving complex optimization problems. Unlike conventional software engineering methods, metaheuristics do not rely on derivative calculations in the search space. Instead, they explore solutions by iteratively refining and adapting their search process. The no-free-lunch (NFL) theorem proves that an optimization scheme cannot perform well in dealing with all optimization challenges. Over the last two decades, a plethora of metaheuristic algorithms has emerged, each with its unique characteristics and limitations. In this paper, we propose a novel meta-heuristic algorithm called ISUD (Individuals with Substance Use Disorder) to solving optimization problems by examining the clinical behaviors of individuals compelled to use drugs. We evaluate the effectiveness of ISUD by comparing it with several well-known heuristic algorithms across 44 benchmark functions of varying dimensions. Our results demonstrate that ISUD outperforms these existing methods, providing superior solutions for optimization problems.

This study optimizes the multi-commodity routing problem in a constrained network, integrating dynamic warehouse management, diverse vehicle ownership options, and congestion management. The model addresses the efficient routing of goods with limited vehicle and warehouse capacities, enabling the addition or removal of warehouses based on demand fluctuations. It incorporates a hybrid fleet strategy, balancing owned and outsourced vehicles to minimize costs while ensuring flexibility. The model also considers network congestion, optimizing routes and schedules to mitigate delays. This approach provides a comprehensive solution for cost-effective and responsive supply chain logistics. In this research, the complexity of the mathematical model and its multi-objective nature led to the use of the epsilon constraint method and the MOGWO and NSGA II algorithms in the model. Solving the model using the mentioned methods showed that the total costs increased with the improvement of the second objective function. This problem has been due to the use of vehicles with higher speeds and higher prices, and also by reducing the risk of transporting products, the total costs have increased again.

This paper presents a method for optimizing the dual-target virtual machine provisioning problem, which is a challenge in cloud data centers. In the cloud environment, it is important to balance the interests of service providers and customers. From the producers’ viewpoint, optimizing energy consumption and reducing costs are essential. From the users’ point of view, it is desirable to achieve an adequate level of quality of service, and network latency is one of the factors that contribute to its reduction. Therefore, optimizing bandwidth usage to reduce network delay is the second important objective considered in this study. To solve this problem, a two-objective method based on a genetic algorithm is presented, which provides near-optimal results in an acceptable time. The evaluations show the superiority of the proposed algorithm in terms of total energy consumption and total traffic in the network compared with methods based on a genetic algorithm, ant colony, greedy FFD algorithm, and randomized deployment method.

Optimization plays a crucial role in enhancing productivity within the industry. Employing this technique can lead to a reduction in system costs. There exist various efficient methods for optimization, each with its own set of advantages and disadvantages. Meanwhile, meta-heuristic algorithms offer a viable solution for achieving the optimal working point. These algorithms draw inspiration from nature, physical relationships, and other sources. The distinguishing factors between these methods lie in the accuracy of the final optimal solution and the speed of algorithm execution. The superior algorithm provides both precise and rapid optimal solutions. This paper introduces a novel agricultural-inspired algorithm named Elymus Repens Optimization (ERO). This optimization algorithm operates based on the behavioral patterns of Elymus Repens under cultivation conditions. Elymus repens is inclined to move to areas with more suitable conditions. In ERO, exploration and exploitation are carried out through Rhizome Optimization Operator and Stolon Optimization Operators. These two supplementary activities are used to explore the problem space. The potent combination of these operators, as presented in this paper, resolves the challenges encountered in previous research related to speed and accuracy in optimization issues. After the introduction and simulation of ERO, it is compared with popular search algorithms such as Gravitational Search Algorithm (GSA), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and Firefly Algorithm (FA). The solution of 23 benchmark functions demonstrates that the proposed algorithm is highly efficient in terms of accuracy and speed.

The multi-material size optimization of transmission tower trusses is carried out in the present study. Three real-size examples are designed, and statically analyzed, and the Black Hole Mechanics Optimization (BHMO) algorithm, a recently developed metaheuristic optimizer methodology, is employed. The BHMO algorithm's innovative search strategy, which draws inspiration from black hole quantum physics, along with a robust mathematical kernel based on the covariance matrix between variables and their associated costs, efficiently converges to global optimum solutions. Besides, three alloys of steel are taken into account in these examples for discrete size variables, each of which is defined in the problem by a weighted coefficient in terms of the elemental weight. The results also indicate that using multiple materials or alloys in addition to diverse cross-sectional sizes leads to the lowest possible cost and the most efficient solution.

The problem of allocation of financial resources in projects is one of the most important problems of mathematical optimization. Incorrect allocation of financial resources can lead to project failure, increased costs, and reduced profitability. The importance of this issue has led to the modeling of a financial resource allocation problem for sustainable projects under uncertainty in this article. A fuzzy programming method was used to control model parameters and GSSA, GA, and SSA algorithms were used to solve the model. In the mathematical model, the goal was to optimize the objective function consisting of predicted return, investment risk, and project sustainability. Mathematical calculation results showed that meta-heuristic algorithms have high efficiency in achieving optimal solutions in a short time. so that the average time to solve them was less than 10 seconds. Also, the calculation results showed that increasing the uncertainty rate leads to increasing the value of the objective function and creating a distance from the optimal point. This is due to increasing costs and decreasing profits in sustainable projects. Finally, usage the TOPSIS method, the ranking of solving algorithms was done, and the GSSA algorithm was the most efficient algorithm among other algorithms with a desirability weight of 0.846.

Medical image registration plays an important role in many clinical applications, including the detection and diagnosis of diseases, planning of therapy, guidance of interventions. Multimodal medical image registration is the process of overlapping two or more images taken from the same scene by different modalities and different sensors. Intensity-based methods are widely used in multimodal medical image registration, these techniques register different modality images that have the same content by optimal transformation. The estimation of the optimal transformation requires the optimization of a similarity metric between the images. Recently, various optimization algorithms have been presented that the selection of appropriate optimization algorithms is very important in determining the optimal transformation parameter. The Social Spider Optimization (SSO) algorithm is one of the meta-heuristic methods that prevents premature convergence. In this paper, medical image registration technique is suggested based on the SSO algorithm. The Mutual Information (MI), Normalization of Mutual Information (NMI), and Sum of Squared Differences (SSD) are used separately as cost function (objective function) and the performance of each of these functions is checked in multimodal medical image registration. The simulation results on Brain Web data set affirm the suggested method outperforms classical registration methods in terms of convergence rate, execution time.