A new hybrid multi-objective, multivariable optimizer utilizing Strength Pareto Evolutionary Algorithm (SPEA), Non-dominated Sorting Differential Evolution (NSDE), and Multi-Objective Particle Swarm (MOPSO) has been created and tested. The optimizer features automatic switching among these algorithms to expedite the convergence of the optimal Pareto front in the objective function(s) space. The...

It is observed that the separated design of location for depots and routing for servicing customers often reach a suboptimal solution. So, solving location and routing problem simultaneously could achieve better results. In this paper, waste collection problem is considered with regard to economic and societal objective functions. A non-dominated sorting genetic algorithm (NSGA-II) is used to l...

This chapter presents three MS Excel programs, namely, EMOO (Excel based Multi-Objective Optimization), NDS (Non-Dominated Sorting) and PM (Performance Metrics) useful for Multi-Objective Optimization (MOO) studies. The EMOO program is for finding non-dominated solutions of a given MOO problem. It has both binary-coded and realcoded NSGA-II (Elitist Non-Dominated Sorting Genetic Algorithm), and...

Cloud computing is a model for convenient on-demand user’s access to changeable and configurable computing resources such as networks, servers, storage, applications, and services with minimal management of resources and service provider interaction. Task scheduling is regarded as a fundamental issue in cloud computing which aims at distributing the load on the different resources of a distribu...

Machines are a key element in the production system and their failure causes irreparable effects in terms of cost and time. In this paper, a new multi-objective mathematical model for dynamic cellular manufacturing system (DCMS) is provided with consideration of machine reliability and alternative process routes. In this dynamic model, we attempt to resolve the problem of integrated family (par...

This paper develops a first comparative study of multiobjective algorithms in Multiple Instance Learning (MIL) applications. These algorithms use grammar-guided genetic programming, a robust classification paradigm which is able to generate understandable rules that are adapted to work with the MIL framework. The algorithms obtained are based on the most widely used and compared multi-objective...

In this paper, a multi-objective approach is applied to the design of aperiodic linear arrays of antennas. The adopted procedure is based on a standard Matlab implementation of the Controlled Elitist Non-Dominated Sorting Genetic Algorithm II. Broadside symmetrical arrays of isotropic radiators are considered with both uniform and non-uniform excitations. The work focuses on whether, and in whi...