Thi-Kien Dao

Hybrid Particle Swarm Optimization with Bat Algorithm

In this paper, a communication strategy for hybrid Particle Swarm Optimization (PSO) with Bat Algorithm (BA) is proposed for solving numerical optimization problems. In this work, several worst individuals of particles in PSO will be replaced with the best individuals in BA after running some fixed iterations, and on the contrary, the poorer individuals of BA will be replaced with the finest particles of PSO. The communicating strategy provides the information flow for the particles in PSO to communicate with the bats in BA. Six benchmark functions are used to test the behavior of the convergence, the accuracy, and the speed of the approached method. The results show that the proposed scheme increases the convergence and accuracy more than BA and PSO up to 3% and 47% respectively.

Parallel bat algorithm for optimizing makespan in job shop scheduling problems

Parallel processing plays an important role in efficient and effective computations of function optimization. In this paper, an optimization algorithm based on parallel versions of the bat algorithm (BA), random-key encoding scheme, communication strategy scheme and makespan scheme is proposed to solve the NP-hard job shop scheduling problem. The aim of the parallel BA with communication strategies is to correlate individuals in swarms and to share the computation load over few processors. Based on the original structure of the BA, the bat populations are split into several independent groups. In addition, the communication strategy provides the diversity-enhanced bats to speed up solutions. In the experiment, forty three instances of the benchmark in job shop scheduling data set with various sizes are used to test the behavior of the convergence, and accuracy of the proposed method. The results compared with the other methods in the literature show that the proposed scheme increases more the convergence and the accuracy than BA and particle swarm optimization.

Parallelized Bat Algorithm with a Communication Strategy

The trend in parallel processing is an essential requirement for optimum computations in modern equipment. In this paper, a communication strategy for the parallelized Bat Algorithm optimization is proposed for solving numerical optimization problems. The population bats are split into several independent groups based on the original structure of the Bat Algorithm BA, and the proposed communication strategy provides the information flow for the bats to communicate in different groups. Four benchmark functions are used to test the behavior of convergence, the accuracy, and the speed of the proposed method. According to the experimental result, the proposed communicational strategy increases the accuracy of the BA on finding the near best solution.

Compact Bat Algorithm

Addressing to the computational requirements of the hardware devices with limited resources such as memory size or low price is critical issues. This paper, a novel algorithm, namely compact Bat Algorithm (cBA), for solving the numerical optimization problems is proposed based on the framework of the original Bat algorithm (oBA). A probabilistic representation random of the Bat’s behavior is inspired to employ for this proposed algorithm, in which the replaced population with the probability vector updated based on single competition. These lead to the entire algorithm functioning applying a modest memory usage. The simulations compare both algorithms in terms of solution quality, speed and saving memory. The results show that cBA can solve the optimization despite a modest memory usage as good performance as oBA displays with its complex population-based algorithm. It is used the same as what is needed for storing space with six solutions.

A Communication Strategy for Paralleling Grey Wolf Optimizer

In this paper, a communication strategy for the parallelized Grey Wolf Optimizer is proposed for solving numerical optimization problems. In this proposed method, the population wolves are split into several independent groups based on the original structure of the Grey Wolf Optimizer (GWO), and the proposed communication strategy provides the information flow for the wolves to communicate in different groups. Four benchmark functions are used to test the behavior of convergence, the accuracy, and the speed of the proposed method. According to the experimental results, the proposed communicational strategy increases the speed and accuracy of the GWO on finding the best solution is up to 75% and 45% respectively in comparison with original method.

Compact Artificial Bee Colony

Another version of Artificial Bee Colony ABC optimization algorithm, which is called the Compact Artificial Bee Colony cABC optimization, for numerical optimization problems, is proposed in this paper. Its aim is to address to the computational requirements of the hardware devices with limited resources such as memory size or low price. A probabilistic representation random of the collection behavior of social bee colony is inspired to employ for this proposed algorithm, in which the replaced population with the probability vector updated based on single competition. These lead to the entire algorithm functioning applying a modest memory usage. The simulations compare both algorithms in terms of solution quality, speed and saving memory. The results show that cABC can solve the optimization despite a modest memory usage as good performance as original ABC oABC displays with its complex population-based algorithm. It is used the same as what is needed for storing space with six solutions.

Prolonging of the Network Lifetime of WSN Using Fuzzy Clustering Topology

Adequate clustering provides an effective way for prolonging the lifetime of a wireless sensor network (WSN). Most proposed clustering algorithms do not consider the location of the base station. This will lead to the hot spots problem in multi-hop WSNs. In this paper, a fuzzy clustering topology is employed to prolong the lifetime of WSNs. Considering the residual energy, the distances to the base station and influence of neighboring parameters of the sensor nodes, the cluster-head radius are adjusted by fuzzy clustering topology. This helps to decrease the intra-cluster traffic load of sensor nodes closer to the base station or having lower battery level. The uncertainties in the estimation of cluster-head radius are handled by fuzzy logic. Our approach is compared with some popular algorithms in literature, including LEACH, Gupta and CHEF. Our approach performs in various performance metrics, such as First Node Dies (FND), Half of the Nodes Alive (HNA), Last Node Dies (LND) and energy-efficiency metrics. Simulation results show that the proposed method performs better than the other algorithms, up to 54% in certain cases. Therefore, this method is a stable and energy-efficient clustering algorithm can be applied to any real-world WSN applications.

A multi-objective optimal mobile robot path planning based on whale optimization algorithm

Due to the complex physical constraints in working space of robot, optimization for mobile robot operations satisfies not only one criterion but also several criteria. In this paper, a novel multi-objective method for optimal mobile robot path planning based on Whale optimization algorithm (WOA) is proposed. In the proposed method, two criteria of distance and smooth path of the robot path planning issue are transformed into a minimization one. The positions of the target and the obstacles in the environment are considered the fitness for the solution in WOA. The position of the globally best whale in each iteration is selected, and reached by the robot in sequence. In addition, the robot processor updates its information during the motion, and the environment is partially unknown for the robot due to the limit of the detection range of its sensors. Series of simulations are implemented in different static environments for the optimal path when the robot reaches its target. The results show that the proposed method provides the robot reaches its target with colliding free obstacles, and the proposed method may be the alternative method of optimization for robot planning.

Dynamic Diversity Population Based Flower Pollination Algorithm for Multimodal Optimization

Easy convergence to a local optimum, rather than global optimum could unexpectedly happen in practical multimodal optimization problems due to interference phenomena among physically constrained dimensions. In this paper, an altering strategy for dynamic diversity Flower pollination algorithm (FPA) is proposed for solving the multimodal optimization problems. In this proposed method, the population is divided into several small groups. Agents in these groups are exchanged frequently the evolved fitness information by using their own best historical information and the dynamic switching probability is to provide the diversity of searching process. A set of the benchmark functions is used to test the quality performance of the proposed method. The experimental result of the proposed method shows the better performance in comparison with others methods.

Unequal Clustering Formation Based on Bat Algorithm forWireless Sensor Networks

Prolonging the lifetime of the network is an important issue in the design and deployment of wireless sensor networks (WSN). One of the crucial factors to prolong lifetime of WSNs is to reduce energy consumption. In this study, Bat algorithm (BA) is used to find out an optimal cluster formation trying to minimize the total communication distance. Taking into account the hot spot problem in multihop WSNs, the communication distance is modeled by bat’s loudness parameter in our scheme. The communication distance and energy consumption of each node in the cluster can then be optimized with the bat algorithm. The experimental results show that this approach achieves 3% improvement of convergence and accuracy in comparison with Particle swarm optimization.