In nature, the eastern North American monarch population is known for its southward migration during the late summer/autumn from the northern USA and southern Canada to Mexico, covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a new kind of nature-inspired metaheuristic algorithm, called monarch butterfly optimization (MBO), a first of its kind, is proposed in this paper. In MBO, all the monarch butterfly individuals are located in two distinct lands, viz. southern Canada and the northern USA (Land 1) and Mexico (Land 2). Accordingly, the positions of the monarch butterflies are updated in two ways. Firstly, the offsprings are generated (position updating) by migration operator, which can be adjusted by the migration ratio. It is followed by tuning the positions for other butterflies by means of butterfly adjusting operator. In order to keep the population unchanged and minimize fitness evaluations, the sum of the newly generated butterflies in these two ways remains equal to the original population. In order to demonstrate the superior performance of the MBO algorithm, a comparative study with five other metaheuristic algorithms through thirty-eight benchmark problems is carried out. The results clearly exhibit the capability of the MBO method toward finding the enhanced function values on most of the benchmark problems with respect to the other five algorithms. Note that the source codes of the proposed MBO algorithm are publicly available at GitHub (
 https://github.com/ggw0122/Monarch-Butterfly-Optimization
 , C++/MATLAB) and MATLAB Central (
 http://www.mathworks.com/matlabcentral/fileexchange/50828-monarch-butterfly-optimization
 , MATLAB).

Monarch butterfly optimization

An immunity enhanced particle swarm optimization (IEPSO) algorithm, which combines particle swarm optimization (PSO) with the artificial immune system, is proposed for damage detection of structures. Some immune mechanisms, selection, receptor editing and vaccination are introduced into the basic PSO to improve its performance. The objective function for damage detection is based on vibration data, such as natural frequencies and mode shapes. The feasibility and efficiency of IEPSO are compared with the basic PSO, a differential evolution algorithm and a real-coded genetic algorithm on two examples. Results show that the proposed strategy is efficient on determining the sites and the extents of structure damages.

Damage detection based on improved particle swarm optimization using vibration data

Virtual network embedding, which means mapping virtual networks requested by users to a shared substrate network maintained by an Internet service provider, is a key function that network virtualization needs to provide. Prior work on virtual network embedding has primarily focused on maximizing the revenue of the Internet service provider and did not consider the energy cost in accommodating such requests. As energy cost is more than half of the operating cost of the substrate networks, while trying to accommodate more virtual network requests, minimizing energy cost is critical for infrastructure providers. In this paper, we make the first effort toward energy-aware virtual network embedding. We first propose an energy cost model and formulate the energy-aware virtual network embedding problem as an integer linear programming problem. We then propose two efficient energy-aware virtual network embedding algorithms: a heuristic-based algorithm and a particle-swarm-optimization-technique-based algorithm. We implemented our algorithms in C++ and performed side-by-side comparison with prior algorithms. The simulation results show that our algorithms significantly reduce the energy cost by up to 50% over the existing algorithm for accommodating the same sequence of virtual network requests.

/pdf/energy-aware-virtual-network-embedding-3chuhuz9hv.pdf

Energy-aware virtual network embedding

As we know, simulated annealing algorithm with large neighborhoods has greater probability of arriving at a global optimum than a small one has, if the other conditions, i.e., the initial configuration, initial temperature and temperature decreasing rate, are the same. However, the large neighborhood is not always beneficial, such as when the distance between the global optimum and the current solution is smaller than the step size. Therefore a simulated annealing algorithm with adaptive neighborhood is proposed in this paper. The adaptive non-uniform mutation operation borrowed from evolutionary algorithm is incorporated into simulated annealing for new solution generation. The neighborhood size reduces in probability with the progress of algorithm. It nearly covers the whole search space in the initial stage of algorithm in the sense of probability. The search engine only searches a very local neighborhood at the later stage of algorithm. Why to hybridize non-uniform mutation with simulated annealing is also analyzed and demonstrated. The numerical experiments show that the hybridization can greatly enhance the performance and the reliability of simulated annealing algorithm. Further experiments are made for benchmarks with expanding search domains. Satisfiable results are obtained again even if the variable bounds are enlarged to 1000 times. Theoretical analysis and simulation experiments illustrate the consistent excellent performance and the possible application of nonu-SA algorithm.

Simulated annealing algorithm with adaptive neighborhood

Knowledge gained through classification of microarray gene expression data is increasingly important as they are useful for phenotype classification of diseases. Different from black box methods, fuzzy expert system can produce interpretable classifier with knowledge expressed in terms of if-then rules and membership function. This paper proposes a novel Genetic Swarm Algorithm (GSA) for obtaining near optimal rule set and membership function tuning. Advanced and problem specific genetic operators are proposed to improve the convergence of GSA and classification accuracy. The performance of the proposed approach is evaluated using six gene expression data sets. From the simulation study it is found that the proposed approach generated a compact fuzzy system with high classification accuracy for all the data sets when compared with other approaches.

Design of fuzzy expert system for microarray data classification using a novel Genetic Swarm Algorithm

http://www.mmrc.iss.ac.cn/~xgao/paper/realEA.pdf

Evolutionary programming based on non-uniform mutation

Based on some phenomena from human society and nature, we propose a binary affinity genetic algorithm (aGA) by adopting the following strategies: the population is adaptively updated to avoid stagnation; the newly generated individuals will be ensured to survive for some generations in order for them to have time to show their good genes; new individuals and the old ones are balanced to have the advantages of both. In order to quantitatively analyze the selective pressure, the concept of selection degree and a simple linear control equation are introduced. We can maintain the diversity of the evolutionary population by controlling the value of the selection degree. Performance of aGA is further enhanced by incorporating local search strategies.

Xinchao Zhao

Papers

Evolutionary programming based on non-uniform mutation

Affinity genetic algorithm