An important task for classification is feature selection that removes the redundant or irrelevant features from dataset. Multi-objective approach mainly proposed by many researchers. However, these approaches failed to maintain higher accuracy while removing redundancy in features. In this work, a wrapper based technique with hybrid of Multi Objective Honey Badger Algorithm (MO-HBA) and Streng...

We address the problem of optimizing a spacecraft trajectory by using three different multi-objective evolutionary algorithms: i) Non-dominated sorting genetic algorithm, ii) Pareto-based ranking genetic algorithm, and iii) Strength Pareto genetic algorithm. The trajectory of interest is an orbit transfer around a central body when the spacecraft uses a lowthrust propulsion system. We use a Lya...

In the paper, a novel stochastic Multi-Objective Self-Organizing Migrating Algorithm (MOSOMA) is introduced. For the search of optima, MOSOMA employs a migration technique used in a single-objective Self Organizing Migrating Algorithm (SOMA). In order to obtain a uniform distribution of Pareto optimal solutions, a novel technique considering Euclidian distances among solutions is introduced. MO...

The paper presents results of computational experiments in which the impact of design representations on the performance of an evolutionary multiobjective structural design processes was investigated. Specifically, two classes of design representations (i.e., direct representations and generative representations) were used to minimize the total weight and the maximum horizontal displacement of ...

In this article, our interest is focused on the automatic learning of Boolean queries in information retrieval systems (IRSs) by means of multi-objective evolutionary algorithms considering the classic performance criteria, precision and recall. We present a comparative study of four well-known, general-purpose, multi-objective evolutionary algorithms to learn Boolean queries in IRSs. These evo...

A new evolutionary multi-objective crowding algorithm (EMOCA) is evaluated using nine benchmark multiobjective optimization problems, and shown to produce non-dominated solutions with significant diversity, outperforming state-of-the-art multi-objective evolutionary algorithms viz., Non-dominated Sorting Genetic Algorithm – II (NSGA-II), Strength Pareto Evolutionary algorithm II (SPEA-II) and P...

Sustainability is a monumental issue that should be considered in designing a logistics system. In order to incorporate sustainability concepts in our study, a waste collection problem with economic, environmental, and social objective functions was addressed. The first objective function minimized overall costs of the system, including establishment of depots and treatment facilities. Addressi...

This research proposes the various versions of modified cuckoo search (MCS) metaheuristic algorithm deploying the strength Pareto evolutionary algorithm (SPEA) multiobjective (MO) optimization technique in rectangular array geometry synthesis. Precisely, the MCS algorithm is proposed by incorporating the Roulette wheel selection operator to choose the initial host nests (individuals) that give ...

A multi-objective evolutionary algorithm (MOEA) approach is presented in this paper. The algorithm (DFBMOEA) aims to improve convergence of Paretobased MOEAs to the true Pareto optimal set/Pareto front and remove decision maker interaction from the process. A novel distance function is used as a fitness function for MOEA. A range equalisation function and a reference vector are utilised to elim...