Multiobjective optimisation has traditionally focused on problems consisting of 2 or 3 objectives. Real-world problems often require the optimisation of a larger number of objectives. Research has shown that conclusions drawn from experimentations ...

Recent studies show that evolutionary optimizers are effective tools in solving real-world problem with complex and competing specifications. As more advanced multiobjective evolutionary optimizers (MOEO) are being designed and proposed, the issue of ...

In this paper, a new type of Multi-Objective Problems (MOPs) is introduced and formulated. The new type is an outcome of a motivation to find optimal solutions for different MOPs, which are coupled through communal components. Therefore, in such cases a ...

Optimization in changing environment is a challenging task, especially when multiple objectives are to be optimized simultaneously. The basic idea to address dynamic optimization problems is to utilize history information to guide future search. In this ...

Evolutionary Multi-objective Optimization (EMO) is expected to be a powerful optimization framework for real world problems such as engineering design. Recent progress in automatic control and instrumentation provides a smart environment called Hardware ...

Most real-world optimization problems involve objectives, constraints, and parameters which constantly change with time. Treating such problems as a stationary optimization problem demand the knowledge of the pattern of change a priori and even then the ...

With the popularity of efficient multi-objective evolutionary optimization (EMO) techniques and the need for such problem-solving activities in practice, EMO methodologies and EMO research and application have received a great deal of attention in the ...

In our recent publication [1], we began with an understanding that many real-world applications of multi-objective optimization involve a large number (10 or more) of objectives but then, existing evolutionary multi-objective optimization (EMO) methods ...

The scalability of EMO algorithms is an issue of significant concern for both algorithm developers and users. A key aspect of the issue is scalability to objective space dimension, other things being equal. Here, we make some observations about the ...

Research within the area of Evolutionary Multi-objective Optimization (EMO) focused on two- and three-dimensional objective functions, so far. Most algorithms have been developed for and tested on this limited application area. To broaden the insight in ...

Many-objective optimization refers to optimization problems with a number of objectives considerably larger than two or three. In this paper, a study on the performance of the Fast Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) for handling ...

The article presents a framework for the resolution of rich vehicle routing problems which are difficult to address with standard optimization techniques. We use local search on the basis on variable neighborhood search for the construction of the ...

This paper presents a new approach for vehicle routing problems (VRPs), which are defined as problems of minimizing the total travel distance. The proposed approach treats VRPs asmulti-objective problems using the concept of multiobjectivization. The ...

Since the pioneer work of Evolution Strategies, experiment-based optimization is one of the promising applications of evolutionary computation. Recent progress in automatic control and instrumentation provides a smart environment called Hardware In the ...

In this paper a procedure for the multi-objective optimization of an automotive cooling duct is described. The two objectives considered are the minimization of the pressure drop between the inlet and the outlet of the duct and the maximization of the ...

This paper presents results of extensive computational experiments in which evolutionary multiobjective algorithms were used to find Pareto-optimal solutions to a complex structural design problem. In particular, Strength-Pareto Evolutionary Algorithm 2 (...

Considering evolutionary multiobjective algorithms for improving single objective optimization problems is focused in this work on introducing the concept of helper objectives in a computational mechanics problem: the constrained mass minimization in ...

Genetic algorithms are one of the state-of-the-art metaheuristic techniques for optimal design of capital-intensive infrastructural water networks. They are capable of finding near optimal cost solutions to these problems given certain cost and ...

In this paper, the design of systems using mechanical or electrical energy-transformation devices is treated as a knapsack problem. Due to the well-known NP-hard complexity of the knapsack problem, a combination of integer linear programming and ...

This paper presents a multicriterion algorithm for dealing with joint facility location and network design problems, formulated as bi-objective problems. The algorithm is composed of two modules: a multiobjective quasi-Newton algorithm, that is used to ...