Bat Algorithm: Literature Review and Applications

Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last 3 years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarized briefly here. Further research topics are also discussed.Comment: 10 page

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

New Hybrid Non-Dominated Sorting Differential Evolutionary Algorithm

This paper presents a new multi objective optimization algorithm with the aim of complete coverage, faster global convergence and higher solution quality. In this technique, the high-speed characteristic of particle swarm optimization (PSO) is combined with non-dominated differential evolutionary (NSDE) and an efficient multi objective optimization algorithm is created. This method posses high convergence characteristic in quite less execution times. Generating fewer populations to find the Pareto front also makes the proposed algorithm use less memory. For the purpose of performance evaluation, the algorithm is verified with four benchmarking functions on its global optimal search ability and compared with two recognized algorithm to assess its diversity. The capability of the suggested algorithm in solving practical engineering problems such as power system protection is also studied and the results are discussed in detail