Application of opposition-based learning concepts in reducing the power consumption in wireless access networks

The reduction of power consumption in wireless access networks is a challenging and important issue. In this paper, we apply Opposition-Based Learning (OBL) concepts for reducing the power consumption of LTE base stations. More specifically, we present a new Modified Biogeography Based Optimization (BBO) algorithm enhanced with OBL techniques. We apply both the original BBO and the new Modified Opposition BBO (MOBBO) to network design cases to the city of Ghent, Belgium, with 75 possible LTE base station locations. We optimize the network towards two objectives: coverage maximization and power consumption minimization. Preliminary results indicate the advantages and applicability of our approach

Modified Biogeography Based Optimization (MBBO)

Biogeography based optimization is most familiar meta-heuristic optimization technique based on biogeography concept. In BBO, a solution of any problem is the habitat and the features of that habitat are suitability index variable (SIV). The SIV values are used by transition operators (migration and mutation).In this paper, we proposed modified BBO (MBBO) which improves the transition operators by introducing exponential average of best solutions. We applied MBBO and some other optimization algorithms (such as BBO, Blended BBO, GA and PSO) on 19 benchmark functions to demonstrate the performance. The proposed MBBO shows outperform on most of the functions

Optimal Design of Damping Control of Oscillations in Power System Using Power System Stabilizers with Novel Improved BBO Algorithm

Studies on power system stability are necessary for power network development & operation. Due to the great dimensionality and complexity of contemporary power systems, its significance has increased. The stability of an interconnected power system is seriously threatened by power system oscillation. Numerous strategies based on contemporary control theory, intelligent control, and optimization methods have been applied to the Power system stabilizers (PSSs) design problem recently. Each categorization contains a number of design techniques that increase the PSS's effectiveness and sturdiness in damping off low frequency vibrations. This work presents a new Modified and Improved Biogeography-Based Optimization (MIBBO) method to increase the optimization effectiveness of the usual Biogeography-Based Optimization (BBO) technique applied for the optimization of the parameters of the PSSs & Proportional Integral Derivative (PID) controller under the non-linear loading (NLL) conditions. The performance parameters which are obtained by the MIBBO based controller are compared with the results of normal BBO Method, Particle Swarm Optimization method (PSO) and Adaptation Law (AL) method. To justify the success and correctness of the proposed control approach, Matlab simulation results-based study of all the above-mentioned techniques is made and reported

Modified Biogeography-Based Optimization with Local Search Mechanism

Biogeography-based optimization (BBO) is a new effective population optimization algorithm based on the biogeography theory with inherently insufficient exploration capability. To address this limitation, we proposed a modified BBO with local search mechanism (denoted as MLBBO). In MLBBO, a modified migration operator is integrated into BBO, which can adopt more information from other habitats, to enhance the exploration ability. Then, a local search mechanism is used in BBO to supplement with modified migration operator. Extensive experimental tests are conducted on 27 benchmark functions to show the effectiveness of the proposed algorithm. The simulation results have been compared with original BBO, DE, improved BBO algorithms, and other evolutionary algorithms. Finally, the performance of the modified migration operator and local search mechanism are also discussed

A modified migration model biogeography evolutionary approach for electromagnetic device multiobjective optimization

Inthispaper, we present anefficient androbust algorithm for multiobjective optimization of electromagnetic devices.Therecentlydeveloped biogeography-based optimization (BBO) is modified byadapting its migration model function so as to improve its convergence.The proposed Modified Migration Model biogeography-based optimization (MMMBBO) algorithm is applied into the optimal geometrical design of an electromagnetic actuator. This multiobjective optimization problem is solved by maximizing the output force as well as minimizing the total weight of the actuator. The comparison between the optimization results using BBO and MMMBBO shows the superiority of the proposed approach

Interactive Markov Models of Evolutionary Algorithms

This paper introduces a Markov model for evolutionary algorithms (EAs) that is based on interactions among individuals in the population. This interactive Markov model has the potential to provide tractable models for optimization problems of realistic size. We propose two simple discrete optimization search strategies with population-proportion-based selection and a modified mutation operator. The probability of selection is linearly proportional to the number of individuals at each point of the search space. The mutation operator randomly modifies an entire individual rather than a single decision variable. We exactly model these optimization search strategies with interactive Markov models. We present simulation results to confirm the interactive Markov model theory. We show that genetic algorithms and biogeography-based optimization perform better with the addition of population-proportion-based selection on a set of real-world benchmarks. We note that many other EAs, both new and old, might be able to be improved with this addition, or modeled with this method

Interactive Markov Models of Evolutionary Algorithms

This paper introduces a Markov model for evolutionary algorithms (EAs) that is based on interactions among individuals in the population. This interactive Markov model has the potential to provide tractable models for optimization problems of realistic size. We propose two simple discrete optimization search strategies with population-proportion-based selection and a modified mutation operator. The probability of selection is linearly proportional to the number of individuals at each point of the search space. The mutation operator randomly modifies an entire individual rather than a single decision variable. We exactly model these optimization search strategies with interactive Markov models. We present simulation results to confirm the interactive Markov model theory. We show that genetic algorithms and biogeography-based optimization perform better with the addition of population-proportion-based selection on a set of real-world benchmarks. We note that many other EAs, both new and old, might be able to be improved with this addition, or modeled with this method

Comparative and comprehensive study of linear antenna arrays’ synthesis

In this paper, a comparative and comprehensive study of synthesizing linear antenna array (LAA) designs, is presented. Different desired objectives are considered in this paper; reducing the maximum sidelobe radiation pattern (i.e., pencil-beam pattern), controlling the first null beamwidth (FNBW), and imposing nulls at specific angles in some designs, which are accomplished by optimizing different array parameters (feed current amplitudes, feed current phase, and array elements positions). Three different optimization algorithms are proposed in order to achieve the wanted goals; grasshopper optimization algorithms (GOA), antlion optimization (ALO), and a new hybrid optimization algorithm based on GOA and ALO. The obtained results show the effectiveness and robustness of the proposed algorithms to achieve the wanted targets. In most experiments, the proposed algorithms outperform other well-known optimization methods, such as; Biogeography based optimization (BBO), particle swarm optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, genetic algorithm (GA), Taguchi method, self-adaptive differential evolution (SADE), modified spider monkey optimization (MSMO), symbiotic organisms search (SOS), enhanced firefly algorithm (EFA), bat flower pollination (BFP) and tabu search (TS) algorithm