In most real world optimization problems several optimization goals have to be considered in parallel. For this reason, there has been a growing interest in Multi-Objective Optimization (MOO) in the past years. Several alternative approaches have been proposed to cope with the occurring problems, e.g. how to compare and rank the different elements. The available techniques produce very good res...

The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most powerful evolutionary algorithms for real-valued single-objective optimization. In this paper, we develop a variant of the CMA-ES for multi-objective optimization (MOO). We first introduce a single-objective, elitist CMA-ES using plus-selection and step size control based on a success rule. This algorithm is compare...

This paper focuses in the selection of an optimal path pair for cooperative diversity based on cross-layer optimization in multihop wireless ad hoc networks. Cross-layer performance indicators, including power consumption, signal-to-noise ratio, and load variance are optimized using multi-objective optimization (MOO) with Pareto method. Consequently, optimization can be performed simultaneously...

this paper proposes per unit coding for combined economic emission load dispatch problem. in the proposed coding, it is possible to apply the percent effects of elements in any number and with high accuracy in objective function. in the proposed per unit coding, each function is transformed into per unit form based on its own maximum value and has a value from 0 to 1. in this paper, particle sw...

A grid-connected electricity-hydrogen integrated energy system (EH-IES) is proposed here for the cooperative analysis of electricity, heat, cooling and hydrogen flows, where carbon emission flow (CEF) introduced to allocate emission. Simultaneously, an improvement multitasking multi-objective optimization (MT-MOO) algorithm applied optimize operation this EH-IES. The EH-IES optimizes operationa...

In many real-world optimization applications there are often a number of conflicting objective functions that are all important to optimize. The purpose of multiobjective optimization (MOO) is to give the decision maker (DM) an understanding of how these functions are conflicting and the possibility to choose an appropriate trade-off between them. There are multiple methods for solving MOO prob...

This paper presents parametric and multi-objective optimization (MOO) approach in optimizing daylight energy consumption by incorporating louvres shading devices depicting three different sky conditions: Birmingham, UK, Jakarta, Indonesia, Sydney, Australia. The study aims to observe the best louvre design configuration for each given context. proposed system iterates logic of device as dynamic...

There are several methods available for modeling sustainable supply chain and logistics (SSCL) issues. Multi-objective optimization (MOO) has been a widely used method in SSCL (SSCLM), nonetheless selecting suitable technique solution is still of interest as model performance highly dependent on decision-making variables the development process. This study provides insights from analysis 95 sch...

despite the growing use of evolutionary multi-objective optimization algorithms in different categories of science, these algorithms as a powerful tool in portfolio optimization and specially solving multi-objective portfolio optimization problem is still in its early stages. in this paper, moeas have been used for solving multi-objective portfolio optimization problem in tehran stock market. f...

Safety barriers are to be designed bring the largest benefit in terms of accidental scenarios consequences mitigation at most reasonable cost. In this paper, we formulate problem identification optimal performance parameters that can same time allow for Natural Technological (NaTech) cost as a Multi-Objective Optimization (MOO) problem. The MOO is solved case study literature, consisting chemic...