Encoding is a fundamental part of any EA. The solution encoding, and its interactions with the EA’s operators, should be designed to minimise arbitrary search bias. A multidimensional encoding suitable for fully specified fuzzy logic rulebases is investigated, and is shown to have better convergence than traditional single-dimensional encoding. By comparison with 2-point and uniform crossover, ...

This paper presents a strategy based on fuzzy logic and lexicographic multi-objective optimization for designing noise abatement procedures aimed at reducing the noise exposure of the population living around the airports. A non-linear multi-objective optimal control problem is formally written and identified. A lexicographic optimization technique is presented for establishing a hierarchical o...

Abstract The Chaos Game Optimization (CGO) has only recently gained popularity, but its effective searching capabilities have a lot of potential for addressing single-objective optimization issues. Despite advantages, this method can tackle problems formulated with one objective. multi-objective CGO proposed in study is utilized to handle the several objectives (MOCGO). In MOCGO, Pareto-optimal...

This study proposes a new multi-item inventory model with hybrid cost parameters under a fuzzy-stochastic constraint and permissible delay in payment. The price and marketing expenditure dependent stochastic demand and the demand dependent the unit production cost are considered. Shortages are allowed and partially backordered. The main objective of this paper is to determine selling price, mar...

This paper proposed a information sharing technique maintaining privacy preservation in distributed data mining. The theoretical principles proposed to develop information sharing technique to find the best choice of objective for goal of the game with distributed manner. The multi-objective evaluation method among the parties data based on best and worst solution has implemented at center of t...

One advantage of evolutionary multiobjective optimization (EMO) algorithms over classical approaches is that many non-dominated solutions can be simultaneously obtained by their single run. In this paper, we propose an idea of using EMO algorithms for constructing an ensemble of fuzzy rule-based classifiers with high diversity. The classification of new patterns is performed based on the vote o...

Evaluation of biomass suppliers is a time-dependent problem that requires assessment of different supply schemes in different periods. This paper presents a hybrid method for evaluating biomass suppliers that combines Intuitionistic Fuzzy Sets (IFS), linear programming (LP) and multi-periodic optimization (MPO). IFS allow evaluators to express their hesitation when they assess alternative suppl...

Pareto optimality is someway ineffective for optimization problems with several (more than three) objectives. In fact the Pareto optimal set tends to become a wide portion of the whole design domain search space with the increasing of the numbers of objectives. Consequently, little or no help is given to the human decision maker. Here we use fuzzy logic to give two new definitions of optimality...

The classification of survival in severe burnt patients is an on-going problem. In this paper we propose a multiobjective optimisation model with constraints to obtain fuzzy classification models based on the criteria of accuracy and interpretability. We also describe a multiobjective evolutionary approach for fuzzy classification based on data with real and discrete attributes. This approach i...