Optimization of a plate-fin heat exchanger design through an improved multi-objective teaching-learning based optimization (MO-ITLBO) algorithm

Teaching-learning-based optimization (TLBO) is a recently developed heuristic algorithm based on the natural phenomenon of teaching-learning process. In the present work, multi-objective improved teaching-learning-based optimization (MO-ITLBO) algorithm is introduced and applied for the multi-objective optimization of plate-fin heat exchangers. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions maintained in an external archive. Minimizing total annual cost and the total weight of heat exchanger as well as minimization of total pressure drop and maximization of heat exchanger effectiveness for specific heat duty requirement are considered as objective functions. Two application examples are presented to demonstrate the effectiveness and accuracy of the proposed algorithm.     

Topology optimization of an offshore jacket structure considering aerodynamic,hydrodynamic and structural forces

The present work focused on the optimization of offshore wind turbine structure which can sustain different environmental conditions and is of the least cost. Size and topology optimization is carried out for the jacket structure from the National Renewable Energy Laboratory (NREL) [used in the Offshore Code Comparison Collaboration Continuation (OC4) project] by using teaching learning-based optimization (TLBO) algorithm and genetic algorithm (GA). The optimization process is carried out in Matlab along with the time-dependent dynamic wind turbine simulation with the aerodynamic, hydrodynamic and structural forces in the fatigue, aerodynamics, structures, and turbulence code (FAST) from NREL. This is an innovative process which can be used to substitute the time-consuming construction of a wind turbine for its analysis. In this work, both static and dynamic analyses are carried out for simultaneous size and topology optimization. The forces applied to the structure are realistic in nature and fatigue analysis is carried out to ensure that the structure does not fail during its design life. This ensures that the simulation is more accurate and realistic as compared with other analysis. The results showed that the TLBO algorithm is effective compared to GA in terms of size and topology optimization. Further, the other state-of-the art algorithms from the Congress on Evolutionary Computation (CEC) such as differential evolution, LSHADE, multi-operator EA-II, effective butterfly optimizer, and unified differential evolution are also implemented and the comparative results of all the algorithms are presented.

     

Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems

A new efficient optimization method, called ‘Teaching–Learning-Based Optimization (TLBO)’, is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the ‘Teacher Phase’ and the second part consists of the ‘Learner Phase’. ‘Teacher Phase’ means learning from the teacher and ‘Learner Phase’ means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems.     

Truss topology optimization with static and dynamic constraints using modified subpopulation teaching–learning-based optimization

In this study, an improved version of the teaching–learning-based optimization (TLBO) algorithm is proposed for truss topology optimization (TTO), with static and dynamic constraints on planar and space trusses. The basic TLBO algorithm is improved to enhance its exploration and exploitation abilities by considering various factors such as the number of teachers, adaptive teaching, tutorial learning and self-motivated learning. The TTO problems are considered with multiple load conditions and subjected to constraints for natural frequencies, element stresses, nodal displacements, Euler buckling criteria and kinematic stability conditions. TTO is achieved with the removal of superfluous elements and nodes from the ground structure, and results in a mass saving. In this method, difficulties arise owing to singular solution and unnecessary analysis; therefore, the finite element model is reformed to resolve these issues. A single-stage optimization approach is used, in which size and topology optimization are considered simultaneously. The results obtained are compared with the best solutions obtained by the algorithm. The results reveal that the modified subpopulation teaching–learning-based optimization (MS-TLBO) algorithm is more effective than other state-of-the-art algorithms.     

Teaching–Learning-Based Optimization: An optimization method for continuous non-linear large scale problems

An efficient optimization method called ‘Teaching–Learning-Based Optimization (TLBO)’ is proposed in this paper for large scale non-linear optimization problems for finding the global solutions. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods.     

Multi-objective sine-cosine algorithm (MO-SCA) for multi-objective engineering design problems

This paper proposes a novel and an effective multi-objective optimization algorithm named multi-objective sine-cosine algorithm (MO-SCA) which is based on the search technique of sine-cosine algorithm (SCA). MO-SCA employs the elitist non-dominated sorting and crowding distance approach for obtaining different non-domination levels and to preserve the diversity among the optimal set of solutions, respectively. The effectiveness of the method is measured by implementing it on multi-objective benchmark problems that have various characteristics of Pareto front such as convex, non-convex and discrete. This proposed algorithm is also checked for the multi-objective engineering design problems with distinctive features. Furthermore, we show the proposed algorithm effectively generates the Pareto front and is easy to implement and algorithmically simple.

     

Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems

An efficient optimization algorithm called teaching–learning-based optimization (TLBO) is proposed in this article to solve continuous unconstrained and constrained optimization problems. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The algorithm is tested on 25 different unconstrained benchmark functions and 35 constrained benchmark functions with different characteristics. For the constrained benchmark functions, TLBO is tested with different constraint handling techniques such as superiority of feasible solutions, self-adaptive penalty, ?-constraint, stochastic ranking and ensemble of constraints. The performance of the TLBO algorithm is compared with that of other optimization algorithms and the results show the better performance of the proposed algorithm.     

A novel geometric pattern-based approach to maximize power output of a wind farm

The placement of wind turbines is crucial for a wind farm because the power generation of wind turbine is greatly affected by the wake effect produced by the upstream turbines. The optimum placement of wind turbines in a wind farm will give the maximum total power output. The effective methodology is imperative to place turbines in such a way that effect of the wake is minimum on a performance of turbines and has a proper utilization of the wind farm area. The present study proposes a novel approach based on the geometrical pattern-inspired placement methodology to locate wind turbines and maximize the power output of a wind farm. In the proposed approach, various geometrical patterns made from circle, hexagon, pentagon and triangle are considered for the numerical investigation. Further, wind behavior is modeled for uniform and variable wind speed from all directions. The present approach is also experimented numerically with and without land availability constraints. Heat transfer search algorithm is used to solve the wind farm layout optimization problem with the proposed approach and the results are compared with other approaches available in the literature. Results show that the proposed geometrical pattern-based approach produces the higher power generation (~4–8%) compared to other approaches for the variable wind speed scenario. In the case of land availability constraints, about 4% higher power generation compared to the grid-based approach is achieved. In order to place turbines with an optimum performance of a wind farm, the present approach can be helpful to wind farm developers.