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 ...

Grid provides global computing infrastructure for users to avail the services supported by the network. The task scheduling decision is a major concern in heterogeneous grid computing environment. The scheduling being an NP-hard problem, meta-heuristic approaches are preferred option. In order to optimize the performance of workflow execution two conflicting objectives, namely makespan (executi...

This paper proposes a novel approach called the Harris’s Hawk Multi-Objective Optimizer (HHMO), which is used for solving reference point multi-objective problems. This algorithm is based on the grey wolf multi-objective optimization algorithm and motivated by the cooperative hunting behaviors of the Harris’s Hawk. These hawks are known as the wolf pack of the sky. The hunting party consists of...

in this paper, a multi-objective method is used to optimize a heat recovery steam generator (hrsg). two objective functions have been used in the optimization, which are irreversibility and hrsg equivalent volume. the former expresses the exergetic efficiency and the latter demonstrates the cost of the hrsg. decision variables are geometric and operational parameters of the hrsg. the results of...

this paper focuses on multi-objective designing of multi-machine thyristor controlled series compensator (tcsc) using strength pareto evolutionary algorithm (spea). the tcsc parameters designing problem is converted to an optimization problem with the multi-objective function including the desired damping factor and the desired damping ratio of the power system modes, which is solved by a spea ...

In this work we consider spatial clustering problem with no a priori information. The number of clusters is unknown, and clusters may have arbitrary shapes and density differences. The proposed clustering methodology addresses several challenges of the clustering problem including solution evaluation, neighborhood construction, and data set reduction. In this context, we first introduce two obj...

In this paper, we deal with the problem of handling solutions in an external archive with the use of a relaxed form of Pareto dominance called ǫ-dominance and a variation of it called paǫ-dominance. These two relaxed forms of Pareto dominance have been used as archiving strategies in some multi-objective evolutionary algorithms (MOEAs). The main objective of this work is to improve the ǫ-domina...

This paper presents a novel network design algorithm for Wireless Local Area Networks (WLANs) considering optimal access point placement and frequency channel assignment problems. The proposed algorithm is a cross-layer approach, accounting the physical layer and the data link layer functionalities of the WLANs in the network design process. Specifically, a multi-objective optimization problem ...

Many economic and financial applications lead (from the mathematical point of view) to deterministic optimization problems depending on a probability measure. These problems can be static (one stage), dynamic with finite (multistage) or infinite horizon, single objective or multiobjective.We focus on one-stage case in multiobjective setting. Evidently, well known results from the deterministic ...

This paper proposes an idea of probabilistically using a scalarizing fitness function in evolutionary multiobjective optimization (EMO) algorithms. We introduce two probabilities to specify how often the scalarizing fitness function is used for parent selection and generation update in EMO algorithms. Through computational experiments on multiobjective 0/1 knapsack problems with two, three and ...