Abstract In many real-world problems there is often the requirement to optimise multiple conflicting objectives in an efficient manner. such can be a mixture of continuous and discrete variables. Herein, we propose new multi-objective algorithm capable optimising both bounded variables The utilises Gaussian processes as surrogates combination with novel distance metric based upon Gower similari...

An Active Robust Optimisation Problem (AROP) aims at finding robust adaptable solutions, i.e. solutions that actively gain robustness to environmental changes through adaptation. Existing AROP studies have considered only a single performance objective. This study extends the Active Robust Optimisation methodology to deal with problems with more than one objective. Once multiple objectives are ...

ABSTRACT In this paper, a method of a Multiobjective Optimisation based on Chebytshev’s method and Genetic Algorithm for the synthesis of planar array antenna was investigated. This method for antenna pattern synthesis could suppress multiple interferences by placing nulls at the directions of the interfering sources and placing the main beam in the direction of the desired signal by controllin...

Modern experiments allow scientists to tackle scientific problems of increasing complexity. Often are characterised by factors that have levels which harder set than others. A possible solution is the use a split-plot design. Many solutions available in literature find optimal designs focus solely on optimising single criterion. Multi-criteria approaches been developed overcome limitations one-...

Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly growing area of investigation. EDMO employs evolutionary approaches to handle problems that have time-varying changes in objective functions, constraints, and/or environmental parameters. Due the simultaneous presence dynamics and multi-objectivity problems, difficulty for has marked increase compared sin...