time-dependent vehicle routing problem is one of the most applicable but least-studied variants of routing and scheduling problems. in this paper, a novel mathematical formulation of time-dependent vehicle routing problems with heterogeneous fleet, hard time widows and multiple depots, is proposed. to deal with the traffic congestions, we also considered that the vehicles are not forced to come...

The abstract is the early stages of ship design, decision ship's main dimensions significantly impacts performance and total cost ownership. This paper focuses on an optimization approach based surrogate models at design. objectives are to minimize power requirements building costs while still satisfying constraints. We compare three approaches models: Kriging, BPNN-PSO (Backpropagation Neural ...

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system — cascade-based operation scenario. For this, we propose new mathematical modeling which goal is to maximize total energy production of plant sub-daily operation, and, simultaneously, water content (volume) reservoirs. solving problem, discuss Multi-objective Evolutionary Swarm Hybridization (MESH)...

This paper presents a bi-objective MIP model for the flexible flow shop scheduling  problem (FFSP) in which the total weighted tardiness and the energy consumption are minimized simultaneously. In addition to considering unrelated machines at each stage, the set-up times are supposed to be sequence- and machine-dependent, and it is assumed that jobs have different release tim...

The gaining returns in line with risks is always a major concern for market play-ers. This study compared the selection of stock portfolios based on the strategy of buying and retaining winning stocks and the purchase strategy based on the level of investment risks. In this study, the two-step optimization algorithms NSGA-II and SPEA-II were used to optimize the stock portfolios. In order to de...

An improved harmony search algorithm for constrained multi-objective optimization problems is proposed in this paper. Inspired by Particle Swarm Optimization, an inductor particle is introduced to speed up the convergence rate of the CMOHS. Two populations are adopted to increase the opportunity of finding the optimal solutions. Numerical experiments are divided into two parts: the first one co...

Machine learning has rapidly evolved during the last decade, achieving expert human performance on notoriously challenging problems such as image classification. This success is partly due to re-emergence of bio-inspired modern artificial neural networks (ANNs) along with availability computation power, vast labelled data and ingenious human-based knowledge well optimisation approaches that can...

An improved harmony search algorithm for constrained multi-objective optimization problems is proposed in this paper. Inspired by Particle Swarm Optimization, an inductor particle is introduced to speed up the convergence rate of the CMOHS. Two populations are adopted to increase the opportunity of finding the optimal solutions. Numerical experiments are divided into two parts: the first one co...

In this paper, an online task scheduling and mapping method based on a fuzzy neural network (FNN) learned by evolutionary multi-objective algorithm (NSGA-II) to jointly optimize the main design challenges of heterogeneous MPSoCs is proposed. approach, first, FNN parameters are trained using NSGA-II-based optimization engine considering including temperature, power consumption, failure rate, exe...

The design of a water distribution system (WDS) involves finding an acceptable trade-off between multiple conflicting objectives, particularly in terms of minimizing cost and maximizing system benefits (such as hydraulic reliability). The goal in multi-objective optimization is to find a set of design solutions which embodies an acceptable trade-off between these costs and benefits, enabling th...