The dynamics of the 3-dimensional flow of magnetized Carreau fluid past a paraboloid surface of revolution is studied through thermal radiation and mass transfer analysis. The impacts of Brownian motion and chemical reaction rate are considered on the flow dynamics. The system of nonlinear PDEs are converted to coupled ODEs by employing suitable transformation relations. The developed ODEs are solved by applying the standard procedure of homotopy analysis method (HAM). The impacts of various interesting parameters on the state variables of the Carreau fluid (velocity components, temperature, concentration, and shear stress) are explained through various graphs and tables. It is found that the horizontal velocity components augment with the rising magnetic parameter and Grashof number values. The fluid temperature augments with the higher values of the pertinent parameters except Prandtl number. The Nusselet number and fluid concentration enhance with the augmenting Brownian motion parameter. The shear stress augments with the rising Grashof number. The agreement of the obtained and published results validate the accuracy of the employed technique.

The China-Pakistan Economic Corridor (CPEC), a major development project in China’s fast-evolving Belt and Road Initiative (BRI), is arguably the most comprehensive of the six BRI economic and infrastructure corridors on land. For Pakistan, it is perceived “as the harbinger of a new era of connectivity and integration” that will not only transform the region’s economic development but also the well-being of Pakistanis. It investigates the impact of CPEC as perceived by the local communities in its ZoI. It also evaluates CPEC’s potential challenges: public support and local communities’ concerns such as their awareness, acceptance, and ownership of CPEC. To accomplish our research objectives, we analyze original microdata from 1,585 respondents living proximate to propose CPEC route(s) of Khyber Pakhtunkhwa province, in Pakistan. After aggregating the data on the basis of age, education, income, and social status, we conduct descriptive, bivariate, and multivariate analyses in an attempt to answer three research questions. We find significantly high level of awareness of CPEC across the board in the older age group of respondents (i.e., 40 years or older), hereinafter referred to as mature, highly educated, that is, respondents with university education. Older respondents expect more environmental and economic benefits than younger respondents, even as the latter expect lower social change and effect on migration than do the former. However, those in the upper social class expect higher economic and political benefits from CPEC, compared with those in the lower social class—an early indication of possible elite capture.

Teaching learning based optimization (TLBO) algorithm is a distinguished nature-inspired population-based meta-heuristic, which is basically designed for unconstrained optimization. TLBO mimics teaching learning process through which learners acquire knowledge from their teachers, and improve their results/grades, accordingly. Stochastic ranking (SR) is a constrained handling technique (CHT), which produces greediness among solutions to improve their fitness values and feasibility. Violation constraint handling (VCH) technique produces more feasibility among the existing superiority of feasibility CHTs due to its additional factor of ranking based on the number of constraints violated (NCV). This work brings in a new variant of SR, namely hybrid stochastic ranking (HSR), which combines SR and VCH. For constrained optimization, the integration of some CHT with TLBO is essential. In this paper, HSR is integrated with TLBO and a new constrained version of TLBO called HSR-TLBO is designed. The efficiency of HSRTLBO is checked on constrained test functions of the suit CEC 2017. The experimental results show that HSR-TLBO got prominent position when compared and ranked with the top four papers and our two newly designed constrained variants of TLBO, MSR-TLBO and MVCH-TLBO, based on the provided budget and ranking criteria of the mentioned suit.

Self-adaptive variants of evolutionary algorithms (EAs) tune their parameters on the go by learning from the search history. Adaptive differential evolution with optional external archive (JADE) and self-adaptive differential evolution (SaDE) are two well-known self-adaptive versions of differential evolution (DE). They are both unconstrained search and optimization algorithms. However, if some constraint handling techniques (CHTs) are incorporated in their frameworks, then they can be used to solve constrained optimization problems (COPs). In an early work, an ensemble of constraint handling techniques (ECHT) is probabilistically hybridized with the basic version of DE. The ECHT consists of four different CHTs: superiority of feasible solutions, self-adaptive penalty, $\epsilon$ -constraint handling technique and stochastic ranking. This paper employs ECHT in the selection schemes, where offspring competes with their parents for survival to the next generation, of JADE and SaDE. As a result, JADE-ECHT and SaDE-ECHT are developed, which are the constrained variants of JADE and SaDE. Both algorithms are tested on 24 COPs and the experimental results are collected and compared according to algorithms’ evaluation criteria of CEC’06. Their comparison, in terms of feasibility rate (FR) and success rate (SR), shows that SaDE-ECHT surpasses JADE-ECHT in terms of FR, while JADE-ECHT outperforms SaDE-ECHT in terms of SR.

Differential Evolution (DE) is one of the prevailing search techniques in the present era to solve global optimization problems. However, it shows weakness in performing a localized search, since it is based on mutation strategies that take large steps while searching a local area. Thus, DE is not a good option for solving local optimization problems. On the other hand, there are traditional local search (LS) methods, such as Steepest Decent and Davidon–Fletcher–Powell (DFP) that are good at local searching, but poor in searching global regions. Hence, motivated by the short comings of existing search techniques, we propose a hybrid algorithm of a DE version, reflected adaptive differential evolution with two external archives (RJADE/TA) with DFP to benefit from both search techniques and to alleviate their search disadvantages. In the novel hybrid design, the initial population is explored by global optimizer, RJADE/TA, and then a few comparatively best solutions are shifted to the archive and refined there by DFP. Thus, both kinds of searches, global and local, are incorporated alternatively. Furthermore, a population minimization approach is also proposed. At each call of DFP, the population is decreased. The algorithm starts with a maximum population and ends up with a minimum. The proposed technique was tested on a test suite of 28 complex functions selected from literature to evaluate its merit. The results achieved demonstrate that DE complemented with LS can further enhance the performance of RJADE/TA.