In the past decades, vehicle routing problem (VRP) has gained considerable attention for its applications in industry, military, and transportation applications. Vehicle routing problem with simultaneous pickup and delivery is an extension of the VRP. This problem is an NP-hard problem; hence finding the best solution for this problem which is using exact method, take inappropriate time, and these methods are not useful in real-world applications. Using meta-heuristic algorithms for calculating and computing the solutions for NP-hard problems is a common method to contrast this challenge. The objective function defined for this problem, is a constrained objective function. In previous algorithms, the penalty method was used as constraint handling technique to define the objective function. Determining the value of parameters and penalty coefficient is not easy in these methods. Moreover, the optimal number of vehicles was not considered in the previous algorithms. So, the user should guess number of vehicles and compare the result with other values for this variable. In this paper, a novel objective function is defined to solve the vehicle routing problem with simultaneous pickup and delivery. This method can find the vehicle routes such that increases the performance of the vehicles and decreases the processes’ costs of transportation. in addition, the optimal number of vehicle in this problem can be calculated using this objective function. Finding the best solution for this optimization problems is an NP-hard and meta-heuristic methods can be used to estimate good solutions for this problem. Then, a constrained version of gravitational search algorithm is proposed. In this method, a fuzzy logic controller is used to calculate the value of the parameters and control the abilities of the algorithm, automatically. Using this controller can balance the exploration and exploitation abilities in the gravitational search algorithm and improve the performance of the algorithm. This new version of gravitational search algorithm is used to find a good solution for the predefined objective function. The proposed method is evaluated on some standard benchmark test functions and problems. The experimental results show that the proposed method outperforms the state-of-the-art methods, despite the simplicity of implementation.

Adaptive Cruise Control (ACC) controls vehicle speed and its distance to the proceeding vehicle in the same lane. In this paper a two-level control architecture is proposed to control both velocity and distance to the leading vehicle by taking advantage of fuzzy logic control (FLC) approach. Then the control parameters were tuned by Gravitational Search Algorithm (GSA) to ensure achieving the fastest and most accurate control response. To evaluate performance of the proposed scheme, a speed profile was developed in simulation based test platform to measure performance of the proposed ACC in different maneuvers including some velocity tests and a distance controlmaneuver. The results revealed that the proposed approach had a stable and fast response which satisfied the requirements of an ACC.

Image compression is one of the important research fields in image processing. Up to now, different methods are presented for image compression. Neural network is one of these methods that has represented its good performance in many applications. The usual method in training of neural networks is error back propagation method that its drawbacks are late convergence and stopping in points of local optimum. Lately, researchers apply heuristic algorithms in training of neural networks. This paper introduces a new training method based on the Gravitational Search Algorithm. Gravitational Search Algorithm is the latest and newest version of swarm intelligence optimization approaches. In this algorithm, the candidate answers in search space are masses that interact with each other by gravitational force and change their positions. Gently, the masses with better fitness obtain more mass and effect on other masses more. In this research, an MLP neural network by GSA method is trained for images compression. In order to efficiency evaluation of the presented compressor, we have compared its performance toward PSO and error back propagation methods in compression of four standard images. The final results show salient capability of the proposed method in training of MLP neural networks.

training of the network. Error back propagation is the most usual training method of neural networks that late convergence and stopping in local optimum points are its weakness. New approach in neural networks training is the usage of heuristic algorithms. This paper suggests a new learning method namely gravitational search algorithm (GSA) in training of neural network for data classification.GSA method is the latest and the most novel version of swarm intelligence optimization methods. This algorithm is inspired fby the law of Newtonian gravity and mass concept in nature. In this paper, a MLP neural network is trained for classification of five benchmark data set by GSA method.Also, the proposed method efficiency in training and testing of neural network compared with those of two training methods error back propagation and particle swarm optimization. Final results showed the GSA method extraordinary performance for data correct classification in most of cases. Also, in these experiments the GSA method produced stable results in all of cases. In addition, the run time of GSA method is shorter than that of the PSO.

In this paper, the effect of incentive-based support schemes on promoting renewable energy resources is investigated in the generation expansion planning (GEP) framework, while the environmental issues are considered. Hence, a comprehensive GEP model incorporated with Feed-in-Tariff (FIT) mechanism is proposed. By considering a restructured environment, gravitational search algorithm (GSA) is employed to find the optimal GEP strategy from a generation company point of view. The numerical studies confirm the fact that sufficient incentive policies intervention is treated as a necessity in the renewable expansion planning resulting in more environmental protection.