Earticle

Biogeography-based optimization (BBO) has shown excellent exploitation ability of the population information for simple-objective optimization problem. But if BBO is directly applied in multi-objective optimization problems (MOPs), optimal solution set gained by BBO has worse diversity and distribution. To overcome these shortcomings, a chaos migration operator is put forwards to improve the diversity of the population. And then based on the new chaos migration operator, Chaos biogeography multi-objective optimization algorithm (CBBMO) is proposed for MOPs. In CBBMO, the chaos migration operator and original mutation operator of BBO are applied to produce the next generation population. The archive is used to conserve the Pareto optimal solutions. The experiment results show that the proposed algorithm CBBMO is feasible and effective for MOPs.

In Heterogeneous Network (HetNet), the small base station such as femtocells are deployed in indoor and multiple business complexes in order to extend the coverage and capacity. The deployment of co-channel (i.e. shared channel) for femtocells along with macrocell induces the severe interference issues. This is because of effects on SINR and thereby capacity become deteriorated. Therefore, this paper investigates the interference mitigation techniques and come out with a proposal to improve the Signal to Interference Noise Ratio (SINR) performance as well as total capacity in Heterogeneous Network (HetNet).The simulation results show that the achieved capacity is maximized than the existing technique.

A Wireless Sensor Network (WSN) is a network of distributed sensors that can collect information from a physical environment. Low-cost sensors are the backbone of a WSN. Maximizing the lifetime of sensor networks is the main challenge of a Wireless Sensor Network (WSN), such as the sensor node of the network needs to be prepared with limited battery power. This paper focuses on finding the optimal sink position. Relay nodes have been introduced in conjunction with the sensor nodes to mitigate network geometric deficiencies since in most other approaches the sensor nodes close to the sink become heavily involved in data forwarding and, thus, their batteries are quickly depleted. A Biogeography-Based Optimization (BBO) algorithm is used to locate the optimal sink position with respect to those relay nodes to make the network more energy efficient. The relay nodes communicate with the sink instead of the sensor nodes. Tests show that this approach can save at least 40% of the energy and prolong the network lifetime.

This paper presents the use of a novel evolutionary algorithm called Biogeography-based optimization (BBO) for the solution of the optimal power flow problem. The objective is to minimize the total fuel cost of generation and environmental pollution caused by fossil based thermal generating units and also maintain an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors and power flow of transmission lines. BBO searches for the global optimum mainly through two steps: Migration and Mutation. In the present work, BBO has been applied to solve the optimal power flow problems on IEEE 30-bus test system with six generating units to test the effectiveness of the proposed method. Satisfactory results obtained from the proposed method were compared to conventional and evolutionary optimization methods.

Optimal reactive power flow (OPRF) reduces power system losses and provides better system voltage control by adjusting the reactive power control variables. It has significant influence on economic and secure operation of power systems. In this article, an improved biogeography-based optimization algorithm based on local search strategy (ILSBBO) is presented for solving optimal reactive power flow. The proposed method integrates local search strategy and selection operation of differential evolution (DE) with migration operator in original BBO to improve the efficiency of migration and overcome the premature convergence in BBO algorithm. It has been applied to standard IEEE 30-bus and IEEE 118-bus test systems, and the comparison results show that the proposed approach is feasible and efficient.

The optimization is an important role in wide geographical distribution of electrical power market, finding the optimum solution for the operation and design of power systems has become a necessity with the increasing cost of raw materials, depleting energy resources and the ever growing demand for electrical energy. In this paper, the real coded biogeography based optimization is proposed to minimize the operating cost with optimal setting of equality and inequality constraints of thermal power system. The proposed technique aims to improve the real coded searing ability, unravel the prematurity of solution and enhance the population assortment of the biogeography based optimization algorithm by using adaptive Gaussian mutation. This algorithm is demonstrated on the standard IEEE-30 bus system and the comparative results are made with existing population based methods.