Comparison between five stochastic global search algorithms for optimizing thermoelectric generator designs

In this study, the best settings of five heuristics are determined for solving a mixed-integer non-linear multi-objective optimization problem. The algorithms treated in the article are: ant colony optimization, genetic algorithm, particle swarm optimization, differential evolution, and teaching-learning basic algorithm. The optimization problem consists in optimizing the design of a thermoelectric device, based on a model available in literature. Results showed that the inner settings can have different effects on the algorithm performance criteria depending on the algorithm. A formulation based on the weighted sum method is introduced for solving the multiobjective optimization problem with optimal settings. It was found that the five heuristic algorithms have comparable performances. Differential evolution generated the highest number of non-dominated solutions in comparison with the other algorithms

Intelligent tourism route optimization based on teaching and learning optimization algorithm

According to the principles of tourism route design and the needs of tourists, the teaching and learning optimization algorithm was improved, and a tourism route optimization method based on the improved teaching and learning optimization algorithm was established. The optimization test of travel routes in Hanzhong area shows that the tourism routes designed by using this algorithm are feasible and efficient, and it has certain practical value for tourism traffic planning, tourism routes design, especially for self-driving tourists to carry out efficient tourism activities

A Posteriori Multiobjective Self-Adaptive Multipopulation Jaya Algorithm for Optimization of Thermal Devices and Cycles

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A novel fuzzy adaptive teaching–learning‑based optimization (FATLBO) for solving structural optimization problems

This paper presents a new optimization algorithm called fuzzy adaptive teaching–learning-based optimization (FATLBO) for solving numerical structural problems. This new algorithm introduces three new mechanisms for increasing the searching capability of teaching–learning-based optimization namely status monitor, fuzzy adaptive teaching–learning strategies, and remedial operator. The performance of FATLBO is compared with well-known optimization methods on 26 unconstrained mathematical problems and five structural engineering design problems. Based on the obtained results, it can be concluded that FATLBO is able to deliver excellence and competitive performance in solving various structural optimization problems

A Teaching-Learning-Based Optimization Algorithm for the Weighted Set-Covering Problem

The need to make good use of resources has allowed metaheuristics to become a tool to achieve this goal. There are a number of complex problems to solve, among which is the Set-Covering Problem, which is a representation of a type of combinatorial optimization problem, which has been applied to several real industrial problems. We use a binary version of the optimization algorithm based on teaching and learning to solve the problem, incorporating various binarization schemes, in order to solve the binary problem. In this paper, several binarization techniques are implemented in the teaching/learning based optimization algorithm, which presents only the minimum parameters to be configured such as the population and number of iterations to be evaluated. The performance of metaheuristic was evaluated through 65 benchmark instances. The results obtained are promising compared to those found in the literature

Optimisation sous contrainte d'un générateur thermoélectrique pour la récupération de chaleur par différents algorithmes heuristiques

La présente étude porte sur le développement et l’optimisation d’un modèle de générateur thermoélectrique placé sur la surface d’une source de chaleur. La particularité de ce modèle est que la source de chaleur est sujette à un flux de chaleur et à une température de surface fixes. L’objectif principal est de développer un modèle de générateur thermoélectrique d’intérêt dans ce contexte particulier qui pourra s’adapter à différentes sources de chaleur et qui pourra inclure différents systèmes de refroidissement. Le modèle a été créé intégralement à l’aide du logiciel Matlab. Un algorithme génétique multi objectif est ensuite utilisé comme outil d’optimisation afin de maximiser les performances tout en minimisant les coûts du générateur thermoélectrique. Les objectifs d’optimisation proposés sont donc de maximiser la puissance électrique et de minimiser le nombre de modules. Lorsqu’un collecteur thermique est inclus au système, il est aussi nécessaire de minimiser la puissance de pompage et l’aire totale d’échange du collecteur. Une première étude considère uniquement la puissance comme objectif d’optimisation afin d’observer l’impact des contraintes de température et de flux de chaleur de la source sur les designs optimaux. Des cas multiobjectifs seront ensuite étudiés avec les différents objectifs énoncés. Finalement, les performances de différents algorithmes d’optimisation heuristiques seront comparées entre eux en utilisant le modèle thermoélectrique développé comme banc d'essai. Les forces et faiblesses de chaque algorithme seront analysées selon divers critères de performance, lorsqu’appliqués à un cas d’optimisation complexe.This study presents a model of a thermoelectric generator placed directly on the surface of a heat source. One unique feature of this model is that the heat source is subject to fixed heat flux and surface temperature that the system must respect. The main objective is to develop this model in this particular context with the possibility to be adapted to any heat source and the option to add a cooling system. The model has been developed entirely on the software Matlab. Then, a genetic algorithm is used to perform an optimisation in order to find the design with the maximal power output and minimal number of thermoelectric modules. With the cooling system included, the total surface of exchange and pumping power is also considered. A preliminary analysis is conducted to analyse the impact of the heat flux and surface temperature constraint on such system. Thereafter, a multi-objective optimisation is performed to find the optimal design considering multiple optimisation objectives. Finally, different heuristic algorithms are compared for solving the thermoelectric model proposed. The performance is discussed using different performance criteria to show the pros and cons of each heuristic algorithm when solving a complex optimisation design problem

Multi-Objective Reactive Power Optimization Including Distributed Generation

 In order to solve the problem of reactive power optimization of distribution network with distributed power supply, the multi-objective reactive power optimization function is established from multiple perspectives, and the equation constraint and inequality constraint equation of power system are considered. Secondly, taking IEEE33 node distribution system with distributed power supply as an example, reactive power optimization of single objective function is carried out to verify that the proposed algorithm has a global convergence and a great advantage in convergence speed. Finally, multi-objective reactive power optimization of distribution network with distributed power supply is carried out. Simulation results demonstrate the effectiveness of the proposed algorithm

Ship Lock Control System Optimization using GA, PSO and ABC: A Comparative Review

This paper presents the comparison of some well-known global optimization techniques in optimization of an expert system controlling a ship locking process. The purpose of the comparison is to find the best algorithm for optimization of membership function parameters of fuzzy expert system for the ship lock control. Optimization was conducted in order to achieve better results in local distribution of ship arrivals, i.e. shorter waiting times for ships and less empty lockages. Particle swarm optimization, artificial bee colony optimization and genetic algorithm were compared. The results shown in this paper confirmed that all these procedures show similar results and provide overall improvement of ship lock operation performance, which speaks in favour of their application in similar transportation problem optimization

Multivariable teaching-learning-based optimization (MTLBO) algorithm for estimating the structural parameters of the buried mass by magnetic data

U ovom radu je predstavljen prirodno utemeljen multivarijabilni algoritam optimizacije poučavanjem-učenjem (MTLBO). MTLBO algoritam tijekom iterativnog postupka može procijeniti najbolje vrijednosti parametara podzemnih struktura (model) u višepredmetnom problemu. Algoritam djeluje u dvije računske faze: fazi učitelja i fazi učenika. Glavna svrha algoritma MTLBO je mijenjati naučene vrijednosti te poboljšavajući tako vrijednosti parametara modela dovesti do optimalnog rješenja. Varijable svakog učenika (model) su: dubina (z), koeficijent amplitude (k), faktor oblika (q), kut učinkovite magnetizacije (θ) i parametri osi (x0). U radu je korištena MTLBO metoda na podacima magnetskih anomalija uzrokovanih podzemnim strukturama jednostavnog geometrijskog oblika, poput sfere i vodoravno postavljenog cilindra. Učinkovitost MTLBO metode također je proučavana na šumom kontaminiranim sintetičkim podacima, budući da su dobiveni prihvatljivi rezultati. MTLBO metoda je primijenjena za interpretaciju četiri profila magnetske anomalije u Iranu, Brazilu i Indiji.This paper presents a nature-based algorithm, titled multivariable teaching-learning-based optimization (MTLBO) algorithm. MTLBO algorithm during an iterative process can estimates the best values of the buried structure (model) parameters in a multi-objective problem. The algorithm works in two computational phases: the teacher phase and the learner phase. The major purpose of the MTLBO algorithm is to modify the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the depth (z), amplitude coefficient (k), shape factor (q), angle of effective magnetization (θ) and axis location (x0) parameters. We employ MTLBO method for the magnetic anomalies caused by the buried structures with a simple geometric shape such as sphere and horizontal cylinder. The efficiency of the MTLBO is also studied by noise corruption synthetic data, as the acceptable results were obtained. We have applied the MTLBO for the interpretation of the four magnetic anomaly profiles from Iran, Brazil and India