Dolphin Swarm Algorithm Research Articles

Palmprint Identification Using Dolphin Optimization

Palm print recognition is a rapidly evolving area in the field of biometrics, providing a high level of security for various applications Advances in scanning technology and software have led to faster and more accurate palm print analysis, we have proposed in this paper a system for palm recognition using the Dolphin Optimization Algorithm (DOA) as a computational technique inspired by nature aimed at solving complex improvement problems. We reduced the number of image features using the Histograms of oriented gradients (HOG) algorithm, we named this method as (DOA) and we also proposed a hybrid method by integrating the DOA algorithm with the Support Vector Machine (SVM) model to improve prediction accuracy by combining DSA's ability to search for global optimal solutions with the effective classification capabilities of SVM, this allows The hybrid approach creates a robust and the proposed hybrid method was named (SVM-DOA), and we also proposed a hybrid method by integrating the DOA algorithm with fuzzy c – mean (FCM) and we named the proposed hybrid method (DOA-fuzzy membership), we verified the validity of the proposed method on public database images of palm print. experiment show that the average accuracy rate of the dolphin swarm algorithm (DSO) is (96.8%), while the average accuracy rate of the proposed hybrid algorithm (DSO-SVM) is (97.8%), and the average accuracy of the proposed hybrid algorithm (DSO-fuzzy membership) is (98.1%).

Optimal Trajectory Planning for Wheeled Mobile Robots under Localization Uncertainty and Energy Efficiency Constraints.

With the rapid development of robotics, wheeled mobile robots are widely used in smart factories to perform navigation tasks. In this paper, an optimal trajectory planning method based on an improved dolphin swarm algorithm is proposed to balance localization uncertainty and energy efficiency, such that a minimum total cost trajectory is obtained for wheeled mobile robots. Since environmental information has different effects on the robot localization process at different positions, a novel localizability measure method based on the likelihood function is presented to explicitly quantify the localization ability of the robot over a prior map. To generate the robot trajectory, we incorporate localizability and energy efficiency criteria into the parameterized trajectory as the cost function. In terms of trajectory optimization issues, an improved dolphin swarm algorithm is then proposed to generate better localization performance and more energy efficiency trajectories. It utilizes the proposed adaptive step strategy and learning strategy to minimize the cost function during the robot motions. Simulations are carried out in various autonomous navigation scenarios to validate the efficiency of the proposed trajectory planning method. Experiments are performed on the prototype “Forbot” four-wheel independently driven-steered mobile robot; the results demonstrate that the proposed method effectively improves energy efficiency while reducing localization errors along the generated trajectory.

An Improved Dolphin Swarm Algorithm Based on Kernel Fuzzy C-Means in the Application of Solving the Optimal Problems of Large-Scale Function

The solution of high dimensional function has always been a hot topic. In this paper, a novel algorithm based on Kernel Fuzzy C-means and dolphin swarm algorithm are proposed to solve high-dimensional functions more accurately. First, to improve the global convergence ability of dolphin swarm algorithm, Kernel Fuzzy C-means is introduced into the algorithm, named as Kernel Fuzzy C-means dolphin swarm algorithm (KFCDSA); Second, the five typical high-dimensional functions are applied to test the performance of the combination of KFCDSA. Finally, some indicators are used to evaluate the performance of different meta-heuristic algorithms. The results show that: the performance of the proposed algorithm exceeds that of the dolphin swarm algorithm and some advanced metaheuristic algorithms considered for comparison based on five different evaluating indicators. Through the test results, it can be concluded that introducing Kernel Fuzzy C-means into dolphin swarm algorithm is an effective improvement and provides a possibility for obtaining global optimal solutions for high-dimensional functions.

Green Deployment Method of Micro Base Station for Ultra-Dense Heterogeneous Cellular Networks Based on Constrained Dolphin Swarm Algorithm

This paper proposes a green deployment method for micro base stations for ultra-dense heterogeneous cellular networks to balance network energy efficiency and electromagnetic radiation and meet certain user service quality. Firstly, a constrained multi-objective mathematical model for the green deployment of the micro base station is established for the two-dimensional communication scenario, with the user rate as the constraint, aiming at maximizing the network energy efficiency and minimizing the average electromagnetic radiation. Then, a multi-objective dolphin swarm algorithm which considering the evolutionary advantages of excellent infeasible solutions and feasible solutions, improving the individual search mechanism in the dolphin group algorithm, combined with the improved two-population strategy is proposed and tested on the CTP test set. It shows that compared with the other three methods, the method has certain advantages in convergence and distribution. Finally, a green deployment method for micro base stations based on constrained multi-objective dolphin swarm algorithm is established. Experiments on nine communication scenarios show that the proposed method can balance network energy efficiency and electromagnetic radiation.

Real power loss reduction by dolphin swarm algorithm

<p>In this work Spinner Dolphin Swarm Algorithm (SDSA) has been applied to solve the optimal reactive power problem. Dolphins have numerous remarkable natural distinctiveness and living behavior such as echolocation, information interactions, collaboration, and partition of labor. Merging these natural distinctiveness and living behavior with swarm intelligence has been modeled to solve the reactive power problem. Proposed Spinner Dolphin Swarm Algorithm (SDSA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.</p>

An Intelligent Game Theory based Intrusion Detection Framework and Enhanced Dolphin Swarm Algorithm (EDSA) based Cluster Head Election for Vehicle Security

An Intelligent Game Theory based Intrusion Detection Framework and Enhanced Dolphin Swarm Algorithm (EDSA) based Cluster Head Election for Vehicle Security

M-Curves path planning model for mobile anchor node and localization of sensor nodes using Dolphin Swarm Algorithm

Location information of a sensor node is the primary concern to process the sensed data in Wireless Sensor Networks (WSNs). The location of the sensor node is used in other domains of sensor network like message routing, node tracking, load balancing. For statically deployed sensor nodes, mobile anchor based localization is an efficient solution. The main challenge in mobile anchor based localization is designing an optimum path for the mobile anchor node considering the coverage, path length and localizability of sensor nodes as the key features. In this paper, we propose a novel path planning approach for mobile anchor based localization called “M-Curves”. Our proposed model promises that all the nodes in the network will receive at least three non-collinear beacon messages for localization. Our proposed trajectory assures full coverage, high localization accuracy as compared to other static models. Also, we optimize the localization process by using Dolphin Swarm Algorithm(DSA). The fitness function used for optimization in DSA, minimizes the localization error of the node in the network.

Modified Dolphin Swarm Algorithm Based on Chaotic Maps for Solving High-Dimensional Function Optimization Problems

In 2016, dolphin swarm algorithm (DSA) that has received sustained research interest due to its simplicity and effectiveness was proposed. However, when solving high-dimensional function optimization problems, DSA is prone to fall into local optimization problems, which leads to low optimization accuracy or even failure. In this paper, to solve this problem, chaotic mapping is introduced into DSA, and chaotic dolphin swarm algorithm (CDSA) is successfully proposed. Based on high-dimensional Rastrigin function, the optimal chaotic map is determined among eight chaotic maps (e.g., Logistic). Then, in view of high-dimensional Levy function, Rotated Hyper-Ellipsoid function and Sum Squares function respectively, the performance of CDSA and that of the state-of-the-art algorithms (e.g. (whale optimization algorithm) WOA) are compared. The results show that the performance of CDSA based on Kent map is best and the performance of CDSA outperform that of the state-of-the-art algorithms considered to be compared. Finally, it is concluded that such a new meta-heuristic algorithm could help to improve the shortcomings of DSA and increase the applied range of DSA.

Solving Large-Scale Function Optimization Problem by Using a New Metaheuristic Algorithm Based on Quantum Dolphin Swarm Algorithm

Meta-heuristic algorithm has been a research hotspot in solving the optimal solution of large-scale functions. However, meta-heuristic algorithms are prone to fall into local optimum problems, such as the recently proposed dolphin swarm algorithm (DSA). To solve this problem, in this study, the quantum search algorithm is introduced into DSA. In addition, to test the performance of the proposed quantum dolphin swarm algorithm (QDSA), six commonly used large-scale functions (e.g. Rotated hyper-ellipsoid function) are taken as examples. Furthermore, some advanced algorithms (e.g. whale optimization algorithm (WOA)) are used for comparison. The results show that the ability of QDSA to obtain global optimal solution is obviously improved compared with DSA, and the performance of QDSA is superior to other algorithms considered for comparison. Finally, it can be concluded that such a novel meta-heuristic algorithm may help to improve the problem of solving the optimal solution of large-scale functions.

An Improved DSA-Based Approach for Multi-AUV Cooperative Search.

Multi-AUV cooperative target search problem in unknown 3D underwater environment is not only a research hot spot but also a challenging task. To complete this task, each autonomous underwater vehicle (AUV) needs to move quickly without collision and cooperate with other AUVs to find the target. In this paper, an improved dolphin swarm algorithm- (DSA-) based approach is proposed, and the search problem is divided into three stages, namely, random cruise, dynamic alliance, and team search. In the proposed approach, the Levy flight method is used to provide a random walk for AUV to detect the target information in the random cruise stage. Then the self-organizing map (SOM) neural network is used to build dynamic alliances in real time. Finally, an improved DSA algorithm is presented to realize the team search. Furthermore, some simulations are conducted, and the results show that the proposed approach is capable of guiding multi-AUVs to achieve the target search task in unknown 3D underwater environment efficiently.

Hybrid fuzzy multi-criteria decision making based multi cluster head dolphin swarm optimized IDS for VANET

Existing Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) based communication suffers from various security and performance issues, hence Cluster based Communication is preferred nowadays. However, Cluster based Communication adds extra overhead and burden on the Cluster Head (CH) in dense network scenarios which eventually introduces delay and hinders network performance. To reduce the overburdening of single CH, a multi cluster head scheme is proposed in which multiple nodes in a cluster can act as CH to share the load of single CH. For a selection of stable CH, Hybrid Fuzzy Multi-criteria Decision making approach (HF-MCDM) is proposed in which Fuzzy Analytic Hierarchy Process (AHP) and TOPSIS methods are clubbed together for optimal decision making. Further because of association of Vehicular Ad-hoc Network (VANET) with life-critical applications, there is a dire need for a security framework to detect various malevolent attacks. Machine Learning based Intrusion Detection System (IDS) like Support Vector Machine (SVM) is one of the approaches for curbing such attacks. These intrusion detection based mechanism can be combined with various existing optimization techniques to improve their performance, and Dolphin Swarm Algorithm is one such approach. Dolphins have many significant biological features like echolocation, exchange of information, coordination, and division of labor. These biological features combined with swarm intelligence can be utilized for optimizing the detection and accuracy of SVM based IDS. So in this paper, a Multi-Cluster Head anomaly based IDS optimized by Dolphin Swarm Algorithm has been proposed and its results are compared with various existing Security frameworks in terms of parameters like false positive, detection rate, detection time, etc. and it is observed that the proposed approach performs better.

Dolphin Swarm Extreme Learning Machine

As a novel learning algorithm for a single hidden-layer feedforward neural network, the extreme learning machine has attracted much research attention for its fast training speed and good generalization performances. Instead of iteratively tuning the parameters, the extreme machine can be seen as a linear optimization problem by randomly generating the input weights and hidden biases. However, the random determination of the input weights and hidden biases may bring non-optimal parameters, which have a negative impact on the final results or need more hidden nodes for the neural network. To overcome the above drawbacks caused by the non-optimal input weights and hidden biases, we propose a new hybrid learning algorithm named dolphin swarm algorithm extreme learning machine adopting the dolphin swarm algorithm to optimize the input weights and hidden biases efficiently. Each set of input weights and hidden biases is encoded into one vector, namely the dolphin. The dolphins are evaluated by root mean squared error and updated by the four pivotal phases of the dolphin swarm algorithm. Eventually, we will obtain an optimal set of input weights and hidden biases. To evaluate the effectiveness of our method, we compare the proposed algorithm with the standard extreme learning machine and three state-of-the-art methods, which are the particle swarm optimization extreme learning machine, evolutionary extreme learning machine, and self-adaptive evolutionary extreme learning machine, under 13 benchmark datasets obtained from the University of California Irvine Machine Learning Repository. The experimental results demonstrate that the proposed method can achieve superior generalization performances than all the compared algorithms.

Dolphin swarm algorithm

By adopting the distributed problem-solving strategy, swarm intelligence algorithms have been successfully applied to many optimization problems that are difficult to deal with using traditional methods. At present, there are many well-implemented algorithms, such as particle swarm optimization, genetic algorithm, artificial bee colony algorithm, and ant colony optimization. These algorithms have already shown favorable performances. However, with the objects becoming increasingly complex, it is becoming gradually more difficult for these algorithms to meet human’s demand in terms of accuracy and time. Designing a new algorithm to seek better solutions for optimization problems is becoming increasingly essential. Dolphins have many noteworthy biological characteristics and living habits such as echolocation, information exchanges, cooperation, and division of labor. Combining these biological characteristics and living habits with swarm intelligence and bringing them into optimization problems, we propose a brand new algorithm named the ‘dolphin swarm algorithm’ in this paper. We also provide the definitions of the algorithm and specific descriptions of the four pivotal phases in the algorithm, which are the search phase, call phase, reception phase, and predation phase. Ten benchmark functions with different properties are tested using the dolphin swarm algorithm, particle swarm optimization, genetic algorithm, and artificial bee colony algorithm. The convergence rates and benchmark function results of these four algorithms are compared to testify the effect of the dolphin swarm algorithm. The results show that in most cases, the dolphin swarm algorithm performs better. The dolphin swarm algorithm possesses some great features, such as first-slow-then-fast convergence, periodic convergence, local-optimum-free, and no specific demand on benchmark functions. Moreover, the dolphin swarm algorithm is particularly appropriate to optimization problems, with more calls of fitness functions and fewer individuals.