We present randomized approximation algorithms for multicriteria traveling salesman problems (TSP), where some objective functions should be minimized while others should be maximized. For the symmetric multi-criteria TSP (STSP), we present an algorithm that computes (2/3−ε, 4+ε) approximate Pareto curves. Here, the first parameter is the approximation ratio for the objectives that should be ma...

We elaborate upon new strategies and heuristics for solving multi-criteria optimization problems via Answer Set Programming (ASP). In particular, we conceive a new solving algorithm, based on conflictdriven learning, allowing for non-uniform descents during optimization. We apply these techniques to solve realistic Linux package configuration problems. To this end, we describe the Linux package...

Scheduling for the flexible job-shops has great importance in both fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization methods because of the high computational complexity. Considering several optimization criteria in this problem will bring on additional complexity and new pr...

The most recent approaches of multi-objective optimization constitute application of meta-heuristic algorithms for which, parameter tuning is still a challenge. The present work hybridizes swarm intelligence with fuzzy operators to extend crisp values of the main control parameters into especial fuzzy sets that are constructed based on a number of prescribed facts. Such parameter-less particle ...

in most of the multi-criteria decision making problems, it is important to have necessary information about relative importance of each criteria. in this group of problems, weights measure the relative importance of preferences of each criteria in comparison with the other decision making criteria. in this research, the nise method has been used for mapping mean-variance efficient frontier. nis...

Answer Set Programming (ASP) features effective optimization capacities based on branch-andbound algorithms. Unlike this, in the area of Satisfiability Testing (SAT) the finding of minimum unsatisfiable cores was put forward as an alternative approach to solving Maximum Satisfiability (MaxSAT) problems. We explore this alternative approach to optimization in the context of ASP. To this end, we ...

The main aim of the present paper is to propose efficient multi-objective optimization algorithms (MOOAs) to tackle truss structure optimization problems. The proposed meta-heuristic algorithms are based on the firefly algorithm (FA) and bat algorithm (BA), which have been recently developed for single-objective optimization. In order to produce a well distributed Pareto front, some improvement...

The fuzzy structured element (FSE) theory is a very useful toolfor dealing with fuzzy multi-criteria decision making (MCDM)problems by transforming the criterion value vectors of eachalternative into the corresponding criterion function vectors. Inthis paper, some concepts related to function vectors are firstdefined, such as the inner product of two function vectors, thecosine of the included ...

Multi-criteria scheduling optimization with genetic algorithms is described. Scheduling optimization methodology provides the planner with a quick and efficient scheduling method and enables him/her to experiment and decide which of the suitable solutions will become the production plan. The scheduling system is composed of a business information system a database, a discrete event simulation m...

Background/introduction One of the key challenges of using Focused Ultrasound for killing cancerous cells in the liver or pancreas is to fully destroy the tumor while at the same time widely sparing healthy tissue such as the ribs, colon, stomach, nerves, etc. This requires optimizing transducer parameters such as phase values and pressure amplitude for each single transducer elements to delive...