In this work, we propose a variant to the Teaching Learning Based Optimization algorithm by incorporating focused learning of students. A student undergoes phase only when it is unable obtain better solution in teacher and expected efficiently utilize limited functional evaluations. The performance evaluated on single objective bound constrained real-parameter numerical optimization problems which have been proposed as part IEEE Congress Evolutionary Computation. has provided competitive...

ABSTRACT This study proposes a multiobjective variant of the phase‐wise teaching learning–based optimization algorithm, namely Non‐dominated Sorting Teaching Learning–Based Optimization (NSpTLBO), for solving job shop scheduling problems with unrelated parallel machines. The proposed technique is integrated no‐wait time heuristic mechanism that reschedules jobs assigned to machines so as minimize constraint violation. Such an approach implemented facilitate determination feasible solutions...

Yin-Yang Pair Optimization is a recently developed metaheuristic technique which searches for the global optima through two stages namely splitting stage and archive stage. A variant of this algorithm proposed in work by converting static updating interval to dynamic one. The performance evaluated on CEC2017 test suite single objective bound constrained real-parameter numerical optimization problems that used Special Session Competition CEC 2017. results obtained reveals its competitive performance.

In this work, a Single Phase Multi-Group Teaching Learning Optimization strategy is proposed which variant of the Based algorithm. The has been used to solve fifteen single objective, bound constrained, computationally expensive benchmark functions that have provided as part one competition in IEEE Congress on Evolutionary Computation 2016. designed specifically handle intensive problems and hence provides competitive results relatively low time complexity.

A novel formulation that can be used with metaheuristic techniques to optimize the biorefinery supply-chain network involving nonlinear cost functions is proposed. repair operator introduced improve potential solutions violate flow rates and capacity constraints. It employs an implicit constraint handling technique ensure satisfaction of constraints mass balances. The proposed framework demonstrated using six in a case study. provides better than standard penalty function approach handle...

ABSTRACTA novel optimization strategy for a compression-absorption cascaded refrigeration system (CACRS) consisting of single effect vapor absorption (VARS) and compression (VCRS), which employs several absorbent solution-refrigerant-refrigerant pairs to consider the combination solution-refrigerant in VARS refrigerants VCRS as an variable, is successfully implemented using various evolutionary techniques. This article analyzes three different with eleven VCRS. A nonlinear objective function...

A large number of computational intelligence algorithms are proposed every year and their performance is usually demonstrated on a cross-section problems which may not provide an ideal comparative analysis. Hence it becomes important to independently evaluate the such complex optimization problems. In this work, we have used benchmark suite IEEE CEC2014 has been extensively in literature study two recently algorithms, viz., JAYA Sine Cosine Algorithm. The analyzed based 9180 instances (30...

In this work, we extend three recently developed computational intelligence techniques, viz., Ying-Yang Pair Optimization, Moth-flame and Grey Wolf Optimization for real variable optimization problems to solve mixed integer problems. As these techniques are primarily continuous variables, strategies handle the variables considered in article thereby leading 9 variants. These variants evaluated on ten unique runs of eighteen constrained their performance is under equal functional evaluations.

In this work, the performance of a variant Teaching Learning Based Optimization algorithm, Single Phase Multi-Group is evaluated on basis its single objective real-parameter numerical optimization problems. These problems have been provided as part one competition in IEEE Congress Evolutionary Computation 2016. It was observed that algorithm competitive to other techniques and satisfactory for many

Moth-Flame is a recently proposed evolutionary optimization technique that has been to solve single objective problems. In this work, we demonstrate the performance of Optimization on two sets benchmark problems were used as part CEC2016. viz., (i) real parameter bound constrained and (ii) computationally expensive The results presented in work enables ranking MFO with respect several other algorithms can aid development variants MFO.

Metaheuristic optimization approaches can be effectively used to model and solve real-world scheduling problems that include making decisions about how allocate a certain amount of capacity or resources (machinery, workers, workspace) tasks customers over time. Many metaheuristic techniques are introduced yearly, their correct assessment improvement aid in solving different real-life problems. In this paper, the performance comparison study three recently proposed techniques, namely African...