This paper investigates three disparate design cases where the newly developed s-Pareto frontier based concept selection paradigm is used to compare competing design concepts under a multiobjective optimization framework. The new paradigm, which was recently introduced by the authors, is based on the principle of Pareto optimality – a principle that defines an important class of optimal solutio...

Pareto local search (PLS) methods are local search algorithms for multiobjective combinatorial optimization problems based on the Pareto dominance criterion. PLS explores the Pareto neighbourhood of a set of non-dominated solutions until it reaches a local optimal Pareto front. In this paper, we discuss and analyse three different Pareto neighbourhood exploration strategies: best, first, and ne...

We consider constraint satisfaction problems where solutions must be optimized according to multiple criteria. When the relative importance of different criteria cannot be quantified, there is no single optimal solution, but a possibly very large set of Pareto-optimal solutions. Computing this set completely is in general very costly and often infeasible in practical applications. We consider s...

Decomposition-based algorithms for multi-objective optimization problems have increased in popularity in the past decade. Although their convergence to the Pareto optimal front (PF) is in several instances superior to that of Pareto-based algorithms, the problem of selecting a way to distribute or guide these solutions in a high-dimensional space has not been explored. In this work, we introduc...

The assignment of multiobjective human resources is a very important phase of the decisionmaking process, especially with respect to research and development projects where performance strongly depends on human resources capabilities. Unfortunately, the input data or related parameters are frequently imprecise / fuzzy owing to incomplete or unobtainable information, which can be represented as ...

In this paper we study the Pareto-optimal solutions in convex multi-objective optimization with compact and convex feasible domain. One of the most important problems in multi-objective optimization is the investigation of the topological structure of the Pareto sets. We present the problem of construction of a retraction function of the feasible domain onto Paretooptimal set, if the objective ...

This article proposes a novel Pareto-based multiobjective meta-heuristic algorithm named non-dominated ranking genetic algorithm (NRGA) to solve multi-facility location-allocation problem. In NRGA, a fitness value representing rank is assigned to each individual of the population. Moreover, two features ranked based roulette wheel selection including select the fronts and choose solutions from ...

This paper proposes a multiobjective harmony search (MOHS) algorithm for optimal power flow (OPF) problem. OPF problem is formulated as a nonlinear constrained multiobjective optimization problem where different objectives and different constraints have been considered. Fast elitist non dominated sorting and crowding distance have been used to find and manage the Pareto optimal front. Finally, ...

Multi-objective problems are a category of optimization problem that contains more than one objective function and these objective functions must be optimized simultaneously. Should the objective functions be conflicting, then a set of solutions instead of a single solution is required. This set is known as Pareto optimal. Multi-objective optimization problems arise in many real world applicati...

The main method of solving multi-objective programming is changing multi-objective programming problem into single objective programming problem, and then get Pareto optimal solution. Conversely, whether all Pareto optimal solutions can be obtained through appropriate method, generally the answer is negative. In this paper, the methods of norm ideal point and membership function are used to sol...