A Modified Binary Pigeon-Inspired Algorithm for Solving the Multi-dimensional Knapsack Problem

The pigeon-inspired optimization algorithm is a category of a newly proposed swarm intelligence-based algorithm that belongs to the population-based solution technique. The MKP is a class of complex optimization problems that have many practical applications in the fields of engineering and sciences. Due to the practical applications of MKP, numerous algorithmic-based methods like local search and population-based search algorithms have been proposed to solve the MKP in the past few decades. This paper proposes a modified binary pigeon-inspired optimization algorithm named (Modified-BPIO) for the 0 - 1 multidimensional knapsack problem (MKP). The utilization of the binary pigeon-inspired optimization (BPIO) for solving the multidimensional knapsack problem came with huge success. However, it can be observed that the BPIO converges prematurely due to lost diversity during the search activities. Given the above, the crossover operator is integrated with the landmark component of the BPIO to improve the diversity of the solution space. The MKP benchmarks from the Operations Research (OR) library are utilized to test the performance of the proposed binary method. Experimentally, it is concluded that the proposed Modified-BPIO has a better performance when compared with the BPIO and existing state-of-the-arts that worked on the same MKP benchmarks.

Swarm Intelligence Based Algorithm for Efficient Routing in VANET

Many recent researchers are working to optimize solutions in the field of Vehicular Adhoc Network. However, none of them has yet claimed that it will fulfill all the challenges of such a dynamic region. VANET in itself is a complete area of study, research and improvements. Most of the researchers and industry consortiums has given their hypothesis and solution that depends on their predefined scenarios but no complete solution has designed until yet. Through this research work, the authors concluded that bioinspired solutions can be used to integrate along with VANET for a much accurate and optimized solution. The performance of VANET depends on various scenarios and due to the unpredictable behavior of the vehicle movement, no concrete solution can be claimed as of now. We incorporated Swarm Intelligence in VANET through the Ant Colony Optimization algorithm and found that the performance of VANET has enhanced by avoiding the entire congested path as it senses the pheromone trail. We have implemented and tested the results using open source software like Instant Veins, Simulation of Urban MObility (SUMO) and MObility model generator for VEhicular networks (MOVE). SUMO has used for testing the traffic simulation and MOVE is used to design model. Python for the script. The OSM used to take a map of Dehradun city. When we performed the experimental setup and found that the result confirms in reducing the traveling time of the nodes, which makes nodes faster and managed even it helps in saving the hydrocarbon fuels. During our approach, we have devised our own algorithm that has improvised the present Ant Colony Optimization algorithm and has concluded that the average traveling time of the nodes minimized through our approach.

Design of An Integration Model for Air Cargo Transportation Network Design and Flight Route Selection

Air cargo transportation is an essential component in the freight transportation market, primarily due to the transportation requirements of time-sensitive products. Air cargo transportation plays an increasingly important role alongside economic development. Cargo flight network design and fleet routing selection significantly affect the performance of the air cargo transportation. In this research, we propose an integrated model simultaneously considering cargo flight network design and the fleet routing selection for the air cargo transportation. Two transportation modes, the direct transportation mode in point-to-point networks and the transshipment mode in hub-and-spoke networks, are compared. In order to solve the proposed optimization problem, a swarm-intelligence-based algorithm is adapted. Numerical experiments were conducted to examine and validate the effectiveness and efficiency of the proposed model and algorithm. The computational results suggest that the proper settings of hub and transshipment route selection in an air cargo transportation network can significantly reduce the transportation cost, which can provide practical managerial insights for the air cargo transportation industry.

A Novel and Secure Hybrid iWD-MASK Algorithm for Enhanced Image Security

With the rapid growth of technology dependency over use of digital data is increasing day by day. In dDesigning digital communication systems to keepmaintain the security of data intact at all levels is important, and is considered as a vital issue in the field of digital computing. Use of digital images for the vast variety of applications in military, medical, and other sectors areis gaining a lot ofgreat popularity. Owing to this, Therefore, there is a rising demand ofto enhance the security of digital imaginge security. In this paper, we have focused on the protection of privacy, protection and proposed one of the swarm intelligence based algorithm known as “intelligent water drop-matrix array symmetric key” (iWD-MASK) for encryption and decryption of digital images. Here, the MASK approach has been implemented to generate the key of sizewhich is 128 bits in size. Also, use of an intelligent water drop algorithm implements the concept of soil particles and velocity to increase the confusion for any attacker(s) if they try to execute any attack on the image. We have also provided the detailed comparison of the proposed iWD-MASK with other hybrid approaches like MAES + Chaos and MASK + Chaos. The proposed algorithm uses three metrics, viz., precision, recall, and F-measure to evaluate the overall performance and the obtained results shows a significant improvement infor encryption and decryption of digital images over other approaches.

An enhanced hybridized artificial bee colony algorithm for optimization problems

Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm. Although it has been proven to be competitive to other population-based algorithms, there still exist some problems it cannot solve very well. This paper presents an Enhanced Hybridized Artificial Bee Colony (EHABC) algorithm for optimization problems. The incentive mechanism of EHABC includes enhancing the convergence speed with the information of the global best solution in the onlooker bee phase and enhancing the information exchange between bees by introducing the mutation operator of Genetic Algorithm to ABC in the mutation bee phase. In addition, to enhance the accuracy performance of ABC, the opposition-based learning method is employed to produce the initial population. Experiments are conducted on six standard benchmark functions. The results demonstrate good performance of the enhanced hybridized ABC in solving continuous numerical optimization problems over ABC GABC, HABC and EABC.

Comprehensive Learning Particle Swarm Optimization for Structural System Identification

This chapter introduces a novel swarm-intelligence-based algorithm named the comprehensive learning particle swarm optimization (CLPSO) to identify parameters of structural systems, which is formulated as a high-dimensional multi-modal numerical optimization problem. With the new strategy in this variant of particle swarm optimization (PSO), historical best information for all other particles is used to update a particle's velocity. This means that the particles have more exemplars to learn from and a larger potential space to fly, avoiding premature convergence. Simulation results for identifying the parameters of a five degree-of-freedom (DOF) structural system under conditions including limited output data, noise polluted signals, and no prior knowledge of mass, damping, or stiffness are presented to demonstrate improved estimation of these parameters by CLPSO when compared with those obtained from PSO. In addition, the efficiency and applicability of the proposed method are experimentally examined by a 12-story shear building shaking table model.