Background and Objectives: Rivers discharge variations, variable rainfall regimes and drought are important reasons for using the water resource management tools in multi reservoir operation. Heuristic optimization methods can be used with different fitness functions; they can be applied for a wide range of water resource management problems specially reservoirs operation systems. Gravitational SEARCH ALGORITHM (GSA) is an evolutionary optimization ALGORITHM based on the law of GRAVITY and mass interactions. In this paper, the ability of this ALGORITHM is investigated for solving the well-known benchmark functions, hydropower-reservoir and ten-reservoir operation system. Materials and Methods: For the verification of new evolutionary ALGORITHM, three well-known benchmarks of Bukin6, Rosenbrock, and Sphere were optimized with GRAVITY SEARCH ALGORITHM and the results were compared with the outcome of well-developed genetic ALGORITHM (GA) and global optima solutions. Then, hydropower-reservoir operation of Karon4 reservoir was optimized with GSA and compared with the results of GA and global solutions. The global solution was obtained from linear programing solving method by using Lingo software. Finally, the ability of GSA was investigated in large scale water resource management problems. In this regard a ten-reservoir system operation was optimized with both GSA and GA and their results were compared with the global solution. It should be noted that the results were reported in different ten runs for three types of problems to ensure that the results are true. Also the function evaluation values of GSA and GA were equal for all optimization problems. Results: The ability of GSA in optimizing of different types of problems are demonstrated with showing the solving results of well-known benchmark functions. The results of Bukin6, Rosenbrock and Sphere problems were close to global optima solutions compared with the outcome of the welldeveloped genetic ALGORITHM results (GA). In single-reservoir hydropower operation, the average values of the objective function were equal 1. 218 and 1. 746 with the GSA and GA, respectively. The global solution equals to 1. 213. Over all, the mean optimum solutions in GSA are better than that of obtained for GA in hydropower-reservoir and ten-reservoir operation problems about 44% and 8% respectively. Conclusion: The results demonstrated the applicability and efficiency of the proposed ALGORITHM in solving the well-known benchmark functions and water-resource optimization problems such as hydropower-reservoir and ten-reservoir operation systems. It is indicated that GSA solutions in different runs are close to the global optima and the ALGORITHM is converged more rapidly than the genetic ALGORITHM.

Background & Aim: One of the important characteristics in the supply chain is the transportation of materials and products between different regions. The distribution of raw materials on each level, like the joints of a chain, connects these levels. Therefore, distribution and transportation have an important role in the cost of products, because improving the vehicle’, s route at distribution and transportation of materials and products in the supply chain will reduce the cost of products. In this article with a case study in one of the logistics supply chains of Iran, customer demand is considered and the model of vehicles routing problem is proposed according to the conditions of the company under study, which is a model of routing problems of open vehicles with a timeline. The objectives of this model are to achieve the lowest cost of distribution as well as the cost of quality degradation due to defects in the transportation system and also the optimal size of the distribution fleet to meet customers demand. Method: method of this reSEARCH’, s studies is library and regarding that these are case study, fields and observation methods are used. To identify the customers and their demands and also characteristics of investigated product and characteristics of vehicles assumed in the problem, reports are extracted from information system of logistic company. To achieve the objectives of this reSEARCH multi-objective Gravitational SEARCH ALGORITHM (MOGSA) has been used to solve open vehicles routing problems along timelines. Optimized navigation routing is done regarding the presented descriptions about GRAVITY force. It means that optimization operation is done by multi-objective Gravitational SEARCH ALGORITHM Findings: The matter that any navigation to serve the customers should traverse which route depends on type of problem which is optimized by multi-objective Gravitational SEARCH ALGORITHM and regarding the achieved beam front, for the optimized navigation base on ALGORITHM a path will be traversed in which some of customers will be in route whom according to their demands and vehicle’, s capacity can be. Conclusion: To decrease the transportation costs, distribution companies’,requirements to exterior assignment of their products transportation is increasing. When the exterior assignment is done vehicles after loading of products and service, don’, t need to return to the main depot and they can return to any of their desired routes after service. In the current study the product distributor company by exterior assignment of distribution operation to another company decreased the costs of vehicle maintenance and amortization into zero and as a result there’, s no need to return the vehicle into the main depot.

Random based inventive ALGORITHMs are being widely used for optimization. An important category of these ALGORITHMs comes from the idea of physical processes or the behavior of beings. A new method for achieving quasi-optimal solutions related to optimization problems in various sciences is proposed in this paper. The proposed ALGORITHM for optimizing the orientation game is a series of optimization ALGORITHMs that are formed with the idea of an old game and the SEARCH operators are an arrangement of players. These players are displaced in a certain space, under the influence of the referee's orders. The best position would be achieved by following the game laws. In this paper, the real version of the ALGORITHM is presented. The optimization results of a set of standard functions confirm the optimal efficiency of the proposed method, as well as the superiority of the proposed method over the other well-known metaheuristic ALGORITHMs.

Considering the high importance of the electricity industry as the infrastructure of other infrastructures of the country, increasing the reliability of the correct operation of the protection programs in the electricity transmission network is considered as one of the measures that can be mentioned in the direction of passive defense. One of these methods is the use of fault current limiters (FCL) in the transmission network. In power systems, the technical and economic benefits of using FCL depend on the type, number, installation locations and optimal parameters of limiters. In the present study, the number, location and impedance of FCLs in the network are determined to achieve various goals such as reducing the short circuit level, then, the minimum number of limiters along with the installation locations and the optimal parameter of each limiter are calculated in two steps using a meta-heuristic ALGORITHM, called the Hybrid Optimization ALGORITHM (PSOGSA) (Combination of the Particle Swarm Optimization ALGORITHM (PSO) and Gravitational SEARCH ALGORITHM (GSA)). In the following, the proposed meta-heuristic ALGORITHM is compared with genetic ALGORITHMs, PSO and GSA. According to the results of the numerical studies conducted to compare the proposed ALGORITHM with GA, PSO and GSA ALGORITHMs, the proposed hybrid ALGORITHM instead of installing more fault current limiters, by increasing its impedance optimally while reducing the total cost of installing this equipment,has better performance in terms of reducing the short circuit level of buses and it converges to the optimal point faster.

This paper presents a compact neural architecture SEARCH method for image classification using the Gravitational SEARCH ALGORITHM (GSA). Deep learning, through multi-layer computational models, enables automatic feature extraction from raw data at various levels of abstraction, playing a key role in complex tasks such as image classification. Neural Architecture SEARCH (NAS), which automatically discovers new architectures for Convolutional Neural Networks (CNNs), faces challenges such as high computational complexity and costs. To address these issues, a GSA-based approach has been developed, employing a bi-level variable-length optimization technique to design both micro and macro architectures of CNNs. This approach, leveraging a compact SEARCH space and modified convolutional bottlenecks, demonstrates superior performance compared to state-of-the-art methods. Experimental results on CIFAR-10, CIFAR-100, and ImageNet datasets reveal that the proposed method achieves a classification accuracy of 98.48% with a SEARCH cost of 1.05 GPU days, outperforming existing ALGORITHMs in terms of accuracy, SEARCH efficiency, and architectural complexity.

In this paper, a new ALGORITHM called Motion Coding Gravitational SEARCH ALGORITHM (MGSA) is proposed to find a moving target using a unmanned aerial vehicles (UAVs). Using the laws of physics and the properties of the earth, each dimension has its own equation of motion based on the type of variable. Many traditional exploratory methods can not achieve the desired solution in high-dimensional spaces to SEARCH for a moving target. The optimization process of the gravitational SEARCH ALGORITHM, which is based on the gravitational interaction between particles, the dependence on the distance and the relationship between mass values, and the fit calculation, make this ALGORITHM unique. In this paper, the proposed MGSA ALGORITHM is proposed to solve the path complexity challenge problem in order to find the moving target through motion coding using UAVs. A set of particles in the path of SEARCH for the target will reach a near-optimal solution through the GRAVITY constant, weight factor, force and distance, which evolved with many SEARCH scenarios in a GSA ALGORITHM. This coded method of motion makes it possible to preserve important particle properties, including the optimum global motion. The results of the existing simulation show that the proposed MGSA improves the detection performance by 12% and the time performance by 1. 71 times compared to APSO. It works better.