This paper addresses the multiobjective vehicle routing problem with time windows (MOVRPTW). The objectives are to minimize the number of vehicles and the total distance simultaneously. Our approach is based on an evolutionary algorithm and aims to find the set of Pareto optimal solutions. We incorporate problem-specific knowledge into the genetic operators. The crossover operator exchanges one...

An approach of reduced-order H∞ controller for a class of linear continuous dynamic systems is presented based on Genetic Algorithm. Necessary and sufficient conditions are given, in terms of linear matrix inequality, for the existence of controller. A rank condition is changed to object function of genetic algorithm(GA). The minimum order nk of the controller and a corresponding parameter pair...

Inspired by the evolutionary strategy and the biological DNA mechanism, a hybrid DNA based genetic algorithm (HDNA-GA) with the population update operation and the adaptive parameter scope operation is proposed for solving parameter estimation problems of dynamic systems. The HDNA-GA adopts the nucleotides based coding and some molecular operations. In HDNA-GA, three new crossover operators, re...

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutatio...

This paper considers the optimum design of flexbeam cross-sections for a full-scale bearingless helicopter rotor, using an efficient hybrid optimization algorithm based on particle swarm optimization, and an improved genetic algorithm, with an effective constraint handling scheme for constrained nonlinear optimization. The basic operators of the genetic algorithm, of crossover and mutation, are...

In this work Traveling salesperson problem is taken as Domain. TSP has long been known to be NP-complete and is a standard example of such problems. Genetic Algorithm (GA) is an approximate algorithm that doesn’t always aim to find the shortest tour but to find a reasonably short tour quickly, which is a search procedure inspired by the mechanisms of biological evolution. In genetic algorithms,...

The genetic algorithm with search area adaptation (GSA) has a capacity for adapting to the structure of solution space and controlling the tradeoff balance between global and local searches, even if we do not adjust the parameters of the genetic algorithm (GA), such as crossover and/or mutation rates. But, GSA needs the crossover operator that has ability for characteristic inheritance ratio co...

Gradient descent techniques such as back propagation have been used effectively to train neural network connection weights; however, in some applications gradient information may not be available. Biologically inspired genetic algorithms provide an alternative. Unfortunately, early attempts to use genetic algorithms to train connection weights demonstrated that exchanging genetic material betwe...

In this paper, chaos based a new arithmetic crossover operator on the genetic algorithm has been proposed. The most frequent issue for the optimization algorithms is stuck on problem's defined local minimum points and it needs excessive amount of time to escape from them; therefore, these algorithms may never find global minimum points. To avoid and escape from local minimums, a chaotic arithme...

In real-world production processes, the same enterprise often has multiple factories or one factory lines, and objectives need to be considered in process. A dual-population genetic algorithm with Q-learning is proposed minimize maximum completion time number of tardy jobs for distributed hybrid flow shop scheduling problems, which have some symmetries machines. Multiple crossover mutation oper...