The Mixed No-Idle Flow-shop Scheduling Problem (MNIFSP) is an extension of flow-shop scheduling, which has practical significance and application prospects in production scheduling. To improve the efficacy solving complicated multiobjective MNIFSP, a MultiDirection Update (MDU) based Multiobjective Particle Swarm Optimization (MDU-MoPSO) proposed this study. For biobjective optimization problem...

Hubs act as intermediate points for the transfer of materials in transportation system. In this study, a novel p-mobile hub location–allocation problem is developed. Hub facilities can be transferred to other hubs next period. Implementation mobile reduce costs opening and closing hubs, particularly an environment with rapidly changing demands. On hand, movement reduces lifespan adds relevant c...

In multiobjective particle swarm optimization (MOPSO), the global-best is randomly selected for each population from a nondominated solution set. However, this Roulette wheel-based global selection ineffective convergence and diversity when problem has numerous decision variables or large number of candidates. Thus, study proposes cluster-based MOPSO (CMOPSO). CMOPSO, similarities between parti...

This study demonstrates how to use grid-connected hybrid PV and biogas energy with a SMES-PHES storage system in nation frequent grid outages. The primary goal of this work is enhance the HRES’s capacity favorably influence economic viability, reliability, environmental impact. net present cost (NPC), greenhouse gas (GHG) emissions, likelihood power outage are among variables that examined. A m...

Task scheduling is a crucial issue in distributed (disbursed) heterogeneous processing environment and significantly influence the performance of the system. The task scheduling problem has been identified to be NP-complete in its universal frame. In this paper the task scheduling problem is investigated using multiple-objective particle (molecule) swarm optimization algorithm with crowded disp...

In this paper, a hybrid meta-heuristic approach is proposed to optimize the mathematical model of a system with mixed repairable and non-repairable components. In this system, repairable and non-repairable components are connected in series. Redundant components and preventive maintenance strategies are applied for non-repairable and repairable components, respectively. The problem is formulate...

<span>Worldwide, the COVID-19 widespread has significant impact on a great number of people. The hospital admittance issue for patients with been optimized by previous research. Identifying symptoms that can be used to determine patient's health status, whether they are dead or alive it is difficult task medical professionals. To solve this issue, multi-objective group counselling optimiz...

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...

This paper proposes an adaptive evolutionary radial basis function (RBF) network algorithm to evolve accuracy and connections (centers and weights) of RBF networks simultaneously. The problem of hybrid learning of RBF network is discussed with the multi-objective optimization methods to improve classification accuracy for medical disease diagnosis. In this paper, we introduce a time variant mul...