The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve...

The Bus Driver Rostering Problem (DRP) consists of assigning bus drivers to daily duties during a planning period. The problem considers hard constraints imposed by institutional and legal requirements. Solutions should as much as possible satisfy soft constraints that qualify rosters according to either the company’s or the drivers’ interests. A bi-objective version of the DRP is considered an...

The relevant literature showed that many heuristic techniques have been investigated for constrained portfolio optimization problem but none of these studies presents multi-objective Scatter Search approach. In this work, we present a hybrid multi-objective population-based evolutionary algorithm based on Scatter Search with an external archive to solve the constrained portfolio selection probl...

This article proposes a new form of algorithm environment for the multiobjective optimization an energy management system in plug-in hybrid vehicles (PHEVs). The surrogate-assisted strength Pareto evolutionary (SSPEA) is developed to optimize power-split control parameters guided by data from physical PHEV and its digital twins (DTs). By introducing “confidence factor,” SSPEA uses fused physica...

Increasing economy’s resistance against the menace of sanctions, various risks, shocks, and internal and external threats are one of the main national policies which can be implemented through bank investments. Investment project selection is a complex and multi-criteria decision-making process that is influenced by multiple and often some conflicting objectives. This paper studies portfolio inve...

Evolutionary algorithms have been recognized to be suitable for extracting approximate solutions of multi-objective problems because of their capability to evolve a set of non-dominated solutions distributed along the Pareto frontier‎. ‎This paper applies an evolutionary optimization scheme‎, ‎inspired by Multi-objective Invasive Weed Optimization (MOIWO) and Non-dominated Sorting (NS) strategi...

In order to help the forging enterprise realize energy conservation and emission reduction, the scheduling problem of furnace heating was improved in this paper. Aiming at the charging problem of continuous heating furnace, a multi-objective furnace charging model with minimum capacity difference and waiting time was established in this paper. An improved strength Pareto evolutionary algorithm ...

Most popular Evolutionary Algorithms for single multi-objective optimisation are motivated by the reduction of the computation time and the resolution larger problems. A promising alternative is to create new distributed schemes that improve the behaviour of the search process of such algorithms. In the multi-objective optimisation problems, more exploration of the search space is required to o...

By replacing the selection component, a well researched evolutionary algorithm for scalar optimization problems (SOPs) can be directly used to solve multi-objective optimization problems (MOPs). Therefore, in most of existing multi-objective evolutionary algorithms (MOEAs), selection and diversity maintenance have attracted a lot of research effort. However, conventional reproduction operators ...