Computation Time Of Evolutionary Algorithms Research Articles

Analysis of Parallel Genetic Algorithm and Parallel Particle Swarm Optimization Algorithm UAV Path Planning on Controller Area Network

Path planning of the unmanned aerial vehicle (UAV) is one of the complex optimization problems due to the model complexity and a high number of constraints. Evolutionary algorithms are a conventional method to solve complex optimization problems with multiple constraints. However, they are time-consuming algorithms which make their implementation difficult. UAV path planning is usually a real-time problem that is needed to solve in limited time steps. Parallelization is one of the most effective methods for reducing the computation time of evolutionary algorithms. In the industrial applications, usually there is a communication platform between devices which can be used for the implementation of the parallelization process. For example, controller area network (CAN) is a protocol used for reliable communication in aerospace systems. Due to the development of this protocol on UAVs and its characteristics, it can be used for parallel optimization of path planning problem. In this paper, an asynchronous distributed multi-master parallel genetic algorithm and a parallel particle swarm optimization algorithm are implemented on CAN bus to improve the speed and performance of the UAV path planning.

Average Drift Analysis and Population Scalability

This paper aims to study how the population size affects the computation time of evolutionary algorithms in a rigorous way. The computation time of an evolutionary algorithm can be measured by either the expected number of generations (hitting time) or the expected number of fitness evaluations (running time) to find an optimal solution. Population scalability is the ratio of the expected hitting time between a benchmark algorithm and an algorithm using a larger population size. Average drift analysis is presented for comparing the expected hitting time of two algorithms and estimating lower and upper bounds on population scalability. Several intuitive beliefs are rigorously analysed. It is prove that (1) using a population sometimes increases rather than decreases the expected hitting time; (2) using a population cannot shorten the expected running time of any elitist evolutionary algorithm on unimodal functions in terms of the time-fitness landscape, but this is not true in terms of the distance-based fitness landscape; (3) using a population cannot always reduce the expected running time on fully-deceptive functions, which depends on the benchmark algorithm using elitist selection or random selection.

A study of drift analysis for estimating computation time of evolutionary algorithms

This paper introduces drift analysis and its applications in estimating average computation time of evolutionary algorithms. Firstly, drift conditions for estimating upper and lower bounds of the mean first hitting times of evolutionary algorithms are presented. Then drift analysis is applied to two specific evolutionary algorithms and problems. Finally, a general classification of easy and hard problems for evolutionary algorithms is given based on the analysis.

Towards an analytic framework for analysing the computation time of evolutionary algorithms

In spite of many applications of evolutionary algorithms in optimisation, theoretical results on the computation time and time complexity of evolutionary algorithms on different optimisation problems are relatively few. It is still unclear when an evolutionary algorithm is expected to solve an optimisation problem efficiently or otherwise. This paper gives a general analytic framework for analysing first hitting times of evolutionary algorithms. The framework is built on the absorbing Markov chain model of evolutionary algorithms. The first step towards a systematic comparative study among different EAs and their first hitting times has been made in the paper.

Dynamic Neighborhood Structures in Parallel Evolution Strategies

Parallelizing is a straightforward approach to reduce the total computation time of evolutionary algorithms. Finding an appropriate communication network within spatially structured populations for improving convergence speed and convergence probability is a difficult task. A new method that uses a dynamic communication scheme in an evolution strategy will be compared with conventional static and dynamic approaches. The communication structure is based on a so-called diffusion model approach. The links between adjacent individuals are dynamically chosen according to deterministic or probabilistic rules. Due to self-organization effects, efficient and stable communication structures are established that perform robustly and quickly on a multimodal test function.