During recent decades, evolutionary algorithms have been widely studied in optimization problems. The multi-objective whale algorithm based on multi-leader guiding is proposed this paper, which attempts to offer a proper framework apply and other swarm intelligence solving adopts several improvements enhance performance. First, search agents are classified into leadership set ordinary by grid m...

vendor managed inventory is a continuous replenishment program that is designed to provide major cost saving benefits for both vendors and retailers. previous research on this area mainly included single objective optimization models where the objective is to minimize the total supply chain costs or to maximize the total supply chain benefits. this paper presents a bi-objective mathematical mod...

: nowadays, organizations are faced with a multitude of project and investment opportunities. despite the importance of various criteria, complexity of multi-objective models and weakness of optimization algorithms often compelled manager to limit the selection criteria or only suffice to financial objects. in this paper, it is endeavored to extend selection criteria by using an efficient optim...

In this paper, a new method based on genetic-fuzzy algorithm for multi-objective optimization is proposed. This method is successfully applied to several multi-objective optimization problems. Two examples are presented: the first example is the optimization of two nonlinear mathematical functions and the second one is the design of PI controller for control of an induction motor drive supplie...

This article addresses the problem of converting a single-objective combinatorial into multi-objective one using Pareto front approach. Although existing algorithms can identify optimal solution in space, they fail to satisfy constraints while achieving performance. To address this issue, we propose artificial bee colony optimization algorithm with classical theme called fitness sharing. approa...

In this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the Selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme and diversity. Scheduling algorithms play an important role in grid computing, parallel tasks Scheduling and sending them to ...

Most of the existing dynamic multi-objective evolutionary algorithms (DMOEAs) are effective, which focuses on searching for approximation Pareto-optimal front (POF) with well-distributed in handling optimization problems (DMOPs). Nevertheless, real-world scenarios, decision maker (DM) may be only interested a portion corresponding POF (i.e., region interest) different instances, rather than who...

The Coati Optimization Algorithm (COA) has emerged as a prominent evolutionary algorithm renowned for its efficacy in addressing real-world problems. Its wide-ranging applicability across diverse domains is testament to exceptional performance and versatility. Compared other algorithms, COA been proven possess excellent global local search capabilities. This paper introduces novel self-organizi...

scheduling for job shop is very important in both fields of production management and combinatorial op-timization. however, it is quite difficult to achieve an optimal solution to this problem with traditional opti-mization approaches owing to the high computational complexity. the combination of several optimization criteria induces additional complexity and new problems. in this paper, we pro...

This paper presents a Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) for the optimal operation of a complex multipurpose and multi-reservoir system. Firstly, MOEA/D decomposes a multi-objective optimization problem into a number of scalar optimization sub-problems and optimizes them simultaneously. It uses information of its several neighboring sub-problems for optimizin...