Constraint handling strategies in Genetic Algorithms application to optimal batch plant design

Optimal batch plant design is a recurrent issue in Process Engineering, which can be formulated as a Mixed Integer Non-Linear Programming(MINLP) optimisation problem involving specific constraints, which can be, typically, the respect of a time horizon for the synthesis of various products. Genetic Algorithms constitute a common option for the solution of these problems, but their basic operating mode is not always wellsuited to any kind of constraint treatment: if those cannot be integrated in variable encoding or accounted for through adapted genetic operators, their handling turns to be a thorny issue. The point of this study is thus to test a few constraint handling techniques on a mid-size example in order to determine which one is the best fitted, in the framework of one particular problem formulation. The investigated methods are the elimination of infeasible individuals, the use of a penalty term added in the minimized criterion, the relaxation of the discrete variables upper bounds, dominancebased tournaments and, finally, a multiobjective strategy. The numerical computations, analysed in terms of result quality and of computational time, show the superiority of elimination technique for the former criterion only when the latter one does not become a bottleneck. Besides, when the problem complexity makes the random location of feasible space too difficult, a single tournament technique proves to be the most efficient one

A hybrid multi-objective evolutionary approach for optimal path planning of a hexapod robot

Hexapod robots are six-legged robotic systems, which have been widely investigated in the literature for various applications including exploration, rescue, and surveillance. Designing hexapod robots requires to carefully considering a number of different aspects. One of the aspects that require careful design attention is the planning of leg trajectories. In particular, given the high demand for fast motion and high-energy autonomy it is important to identify proper leg operation paths that can minimize energy consumption while maximizing the velocity of the movements. In this frame, this paper presents a preliminary study on the application of a hybrid multi-objective optimization approach for the computer-aided optimal design of a legged robot. To assess the methodology, a kinematic and dynamic model of a leg of a hexapod robot is proposed as referring to the main design parameters of a leg. Optimal criteria have been identified for minimizing the energy consumption and efficiency as well as maximizing the walking speed and the size of obstacles that a leg can overtake. We evaluate the performance of the hybrid multi-objective evolutionary approach to explore the design space and provide a designer with an optimal setting of the parameters. Our simulations demonstrate the effectiveness of the hybrid approach by obtaining improved Pareto sets of trade-off solutions as compared with a standard evolutionary algorithm. Computational costs show an acceptable increase for an off-line path planner. © Springer International Publishing Switzerland 2016

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures

Stackelberg game design and operation of a non-cooperative Bi-Level H2 supply chain under cournot equilibrium

This paper proposes a hybrid solution algorithm for the mixed-integer bi-level programming problem (MIBLP) as a mathematical model of the Stackelberg game with the novelty inclusion of multi-followers in competition. The solution strategy considers the MIBLP as a multi-parametric problem knowing that the feasible set of the lower level problem (LLP) of a bi-level programming problem (BLPP) is parametric in terms of the optimization variables of the upper level problem (ULP), under those circumstances is possible to solve each level with a different approach. To tackle the oligopoly (multi-player) competition in the LLP, a general static Cournot equilibrium model is proposed, with the particularities of non-differentiated product with asymmetric cost. The proposed hybrid algorithm implements Differential Evolution to solve the ULP while each feasible population member of each generation execute a MILP Solver to search for feasible LLP individuals sharing the market and looking to maximize their individual profit. The algorithm, which is a new method for the resolution of MIBLP, is illustrated through a modified numerical example from the literature adapted to Energy Markets. Competitive energy markets are the best way to keep prices as low as possible and create a climate that encourages economic growth, job creation and innovation. Demand and supply of energy are best determined through fair and competitive markets, meaning, well-designed competititve markets deliver better results than traditional monopoly markets. Until now, most of the Hydrogen Supply Chains (HSC) designs are treated as problems with single or multiple objectives without any hierarchical conflict, mainly in a centralized monopolic view point. Computational results prove the functionality of the proposed hybrid algoreithm on a ficticious HSC, the evidence highlights the impact between choosing a monopoly or an oligopoly production model.Peer ReviewedPostprint (author's final draft

A hybrid strategy for mixed integer bi-level optimization applied to hydrogen energy supply chain management

This paper addresses a supply chain management problem with consideration of production and distribution activities. The formulation is based on a mixed-integer bi-level programming approach as a mathematical model of the leader-follower game. A hybrid evolutionary-deterministic strategy has been developed and the performance of the solution method is evaluated by numerical experiments based on a ficticious hydrogen energy system. The experimental results obtained show that the resolution method is efficient and promising for dealing with multi-objective optimization cases.The National Council of Science and Technology of Mexico (Consejo Nacional de Ciencia y Tecnologia, CONACYT). Bourse reference: 2018-000003-01EXTF-00046, supported this work.Peer ReviewedPostprint (author's final draft

SC: A NOVEL FUZZY CRITERION FOR SOLVING ENGINEERING AND CONSTRAINED OPTIMIZATION PROBLEMS

In this paper a novel fuzzy convergence system (SC) and its fundamentals are presented. The model was implemented on a monoobjetive PSO algorithm with three phases: 1) Stabilization, 2) generation and breadth-first search, and 3) generation and depth-first. The system SC-PSO-3P was tested with several benchmark engineering problems and with several CEC2006 problems. The computing experience and comparison with previously reported results is presented. In some cases the results reported in the literature are improved