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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, ε -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.

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The performance of search operators varies across the different stages of the search/optimisation process of Evolutionary Algorithms (EA). In general, a single search operator may not do well in all these stages when dealing with different optimization and search problems. To mitigate this, adaptive search operator schemes have been introduced. The idea is that when a search operator hits a difficult patch (under-performs) in the search space, the EA scheme "reacts" to that by potentially calling upon a different search operator. Hence, several multiple-search operator schemes have been proposed and employed within EA. In this paper, a Hybrid Adaptive Evolutionary Algorithm Based on Decomposition (HAEA/D) that employs four different crossover operators is suggested. Its performance has been evaluated on the well-known IEEE CEC'09 test instances. HAEA/D has generated promising results which compare well against several well-known algorithms including MOEA/D, on a number of metrics such as the Inverted Generational Distance (IGD), the hyper-volume, the Gamma and Delta functions. These results are included and discussed in this paper.

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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.

Grey wolf optimization (GWO) algorithm is a relatively recent and novel optimization approach. GWO showed performance improvement over all competing algorithms. However, the relevant literature identified that the primary GWO due to its position update equation shows superiority in exploitation, but is inefficient in exploration. It shows slow convergence and low precision, too. Motivated by the outlined issues in the primary GWO, this work presents two new and improved GWO algorithms. The first proposed variant modifies all the three models, encircling model of prey, position update equation and the hunting equation of canonical GWO. Further, a new parameter is introduced to scale the encircling and position update equations. As a result, the exploration issue of the algorithm is tackled. Unlike the first variant, the second proposed variant does not modify the position update models, but it incorporates Minkowski's information into GWO. To the best of our knowledge, no such modifications to GWO have been done before. The proposed modified versions of GWO are tested on a well-known test functions suit and then compared with different population-based algorithms, including fast evolutionary programming and particle swarm optimization. It was identified from the simulation results that proposed algorithms outperform different algorithms in comparison on majority of problems. The sensitivity study of the proposed algorithms to their various parameters is also provided. INDEX TERMS Population-based search approaches, evolutionary computation, unconstrained optimization, grey wolf optimization, global search, Minkowski's formula. The associate editor coordinating the review of this manuscript and approving it for publication was Huaqing Li.