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A genetic algorithm is one of the best optimization techniques for solving complex nature optimization problems. Different selection schemes have been proposed in the literature to address the major weaknesses of GA i.e., premature convergence and low computational efficiency. This article proposed a new selection operator that provides a better trade-off between selection pressure and population diversity while considering the relative importance of each individual. The average accuracy of the proposed operator has been measured by χ2 goodness of fit test. It has been performed on two different populations to show its consistency. Also, its performance has been evaluated on fourteen benchmark problems while comparing it with competing selection operators. Results show the effective performance in terms of two statistics i.e., less average and standard deviation values. Further, the performance indexes and the GA convergence show that the proposed operator takes better care of selection pressure and population diversity.

Bioremediation and phytoremediation have demonstrated potential for decontamination of petroleum hydrocarbon-impacted soils. The total petroleum hydrocarbons (TPHs) are known to induce phytotoxicity, reduce water retention in soil, associate hydrophobic nature and contaminants' in situ heterogeneous distribution, limit soil nutrient release and reduce soil aeration and compaction. The ageing of TPHs in contaminated soils further hinders the degradation process. Soil amendments can promote plant growth and enhance the TPH removal from contaminated aged soil. In the present experiment, remediation of TPH-contaminated aged soil was performed by Italian ryegrass, with compost (COM, 5%), biochar (BC, 5%) and immobilized microorganisms' technique (IMT). Results revealed that significantly highest hydrocarbon removal (40%) was noted in mixed amendments (MAA) which contained BC + COM + IMT, followed by COM (36%), compared to vegetative control and other treatments. The higher TPH removal in aged soil corresponds with the stimulated rhizospheric effects, as evidenced by higher root biomass (85-159% increase), and bacterial count compared to NA control. Phyto-stimulants actions of biochar and IMT improved seed germination of Italian ryegrass. The compost co-amendment with other treatments showed improvement in plant physiological status. These results suggested that plant growth and TPH removal from aged, contaminated soils using BC, COM and IMT can improve bioremediation efficiency.

The current study was dedicated to finding the effect of soil amendments (biochar and compost) on plants belonging to Poaceae and Fabaceae families. Plants selected for the phytoremediation experiment included wheat (Triticum aestivum), maize (Zea mays), white clover (Trifolium repens), alfalfa (Medicago sativa), and ryegrass (Lolium multiflorum). The physiological and microbial parameters of plants and soil were affected negatively by the 4 % TPHs soil contamination. The studied physiological parameters were fresh and dried biomass, root and shoot length, and chlorophyll content. Microbial parameters included root and shoot endophytic count. Soil parameters included rhizospheric CFUs and residual TPHs. Biochar with wheat, maize, and ryegrass (Fabaceae family) and compost with white clover and alfalfa (Poaceae family) improved plant growth parameters and showed better phytoremediation of TPHs. Among different plants, the highest TPH removal (68.5 %) was demonstrated by ryegrass with compost, followed by white clover with biochar (68 %). Without any soil amendment, ryegrass and alfalfa showed 59.55 and 35.21 % degradation of TPHs, respectively. Biochar and compost alone removed 27.24 % and 6.01 % TPHs, respectively. The interactive effect of soil amendment and plant type was also noted for studied parameters and TPHs degradation.

As an intelligent search optimization technique, genetic algorithm (GA) is an important approach for non-deterministic polynomial (NP-hard) and complex nature optimization problems. GA has some internal weakness such as premature convergence and low computation efficiency, etc. Improving the performance of GA is a vital topic for complex nature optimization problems. The selection operator is a crucial strategy in GA, because it has a vital role in exploring the new areas of the search space and converges the algorithm, as well. The fitness proportional selection scheme has essence exploitation and the linear rank selection is influenced by exploration. In this article, we proposed a new selection scheme which is the optimal combination of exploration and exploitation. This eliminates the fitness scaling issue and adjusts the selection pressure throughout the selection phase. The $$\chi ^2$$ goodness-of-fit test is used to measure the average accuracy, i.e., mean difference between the actual and expected number of offspring. A comparison of the performance of the proposed scheme along with some conventional selection procedures was made using TSPLIB instances. The application of this new operator gives much more effective results regarding the average and standard deviation values. In addition, a two-tailed t test is established and its values showed the significantly improved performance by the proposed scheme. Thus, the new operator is suitable and comparable to established selection for the problems related to traveling salesman problem using GA.

Petroleum hydrocarbons are extensively utilized in petrochemical industries and cause soil deterioration during exploration, transportation, refining and making petroleum products. We hypothesized that the combined use of compost, biochar and bacterial consortia as soil amendments may enhance the rhizoremediation potential of ryegrass by strengthening the plant rhizospheric effect for efficient total petroleum hydrocarbon removal. The present study focused on phytoremediation of hydrocarbons in spiked contaminated soil amended with biochar (5% v/v), and compost (5% v/v). Spiked soil was inoculated with consortia of four hydrocarbon degrading bacterial strains (Pseudomonas poae, Actinobacter bouvetii, Stenotrophomonas rhizophila and Pseudomonas rhizosphaerae). The spiked soil was prepared by spiking agricultural soil with 3.4% (w/w) of crude oil. Italian ryegrass (60 seeds pot−1) were sown and plants were harvested after 75 days. The highest hydrocarbon removal (85%) was observed in spiked soil amended with compost, biochar and consortia. Bacterial inoculation with biochar and compost showed significantly higher hydrocarbon degradation as compared to all other treatments. Highest TPHs degrading bacteria (5.74 × 107 cells g−1 of soil) were observed in rhizosphere of spiked soil amended with compost, biochar and consortia. The organic amendments improved plant growth and bacterial count in rhizosphere which resulted in higher removal of hydrocarbons. We concluded that plant-microbe interactions together with the organic soil amendments offer an emerging trend for remediation of hydrocarbons. Rhizoremediation is a green solution to overcome the quandary of total petroleum hydrocarbon contamination in soil.

Neighbor balanced designs are robust to neighbor effects, therefore, these designs are used to balance out the neighbor effects. If a large number of experimental material is required for combinatorial neighbor balance then partially neighbor balanced designs should be recommended. In this study, some useful generators are developed to obtain the partially neighbor balanced designs in linear blocks of sizes 3–7. Keywords: Linear block, Neighbor effects, Neighbor balanced designs, Partially neighbor balanced designs

In this study, we developed a model which elaborates relationship among efficiency of an estimator and survey cost. This model is based on a multiobjective optimization programming structure. Survey cost and efficiency of related estimator(s) lie in different directions, i.e., if one increases, the other decreases. The model presented in this study computes cost for a desired level of efficiency on various characteristics (goals). The calibrated model minimizes the cost for the compromise optimal sample selection from different strata when characteristic j is subject to achieve at least 1−αj level of efficiency of its estimator. In the first step, the proposed model minimizes the variance for a fixed cost, and it then finds the rise in cost for an αj percent rise in efficiency of any characteristic j. The resultant model is a multiobjective compromise allocation goal programming model.

In the present paper, a new Gamma cost function is proposed for an optimum allocation in multivariate stratified random sampling with linear regression estimator. Extended lexicographic goal programming is used for solution of multi-objective non-linear integer allocation problem. A real data set is used to illustrate the application.

In the paper we develop a two stage scenario-based stochastic programming model for water management in the Indus Basin Irrigation System (IBIS). We present a comparison between the deterministic and scenario-based stochastic programming model. Our model takes stochastic inputs on hydrologic data i.e. inflow and rainfall. We divide the basin into three rainfall zones which overlap on 44 canal commands. Data on crop characteristics are taken on canal command levels. We then use ten-daily and monthly time intervals to analyze the policies. This system has two major reservoirs and a complex network of rivers, canal head works, canals, sub canals and distributaries. All the decisions on hydrologic aspects are governed by irrigation and agricultural development policies. Storage levels are maintained within the minimum and maximum bounds for every time interval according to a power generation policy. The objective function is to maximize the expected revenue from crops production. We discuss the flexibility of two stochastic optimization models with varying time horizon.

Biodegradation and biomass production are affected by numerous environmental factors including pH, oxygen availability and presence of pollutants. The present study, for the first time, elucidated the effects of nutrients and light on mycodegradation of petroleum hydrocarbons in diesel oil. Seven fungal strains (Aspergillus terreus FA3, Aspergillus niger FA5, Aspergillus terreus FA6, Penicillium chrysogenum FP4, Aspergillus terreus FP6, Aspergillus flavus FP10, and Candida sp. FG1) were used for hydrocarbon degradation under static conditions, in four combinations of nutrient media and illuminance for 45 days. Highest degradation was achieved by Aspergillus terreus FA6 and Candida sp. FG1 under both conditions of light and dark, with nutrient deprived HAF (Hydrocarbon adopted fungi) broth. Under HAF/Dark diesel oil degradation by FA6 and FG1 was 87.3% and 84.3% respectively, while under HAF/Light both FA6 and FG1 performed 84.3% biodegradation. The highest biomass was produced by Aspergillus flavus FP10 in PDB (Potato dextrose broth)/Dark (109.3 mg). Fungal degradation of petroleum hydrocarbons was negatively affected by the presence of other simpler-to-degrade carbon sources in the medium. The biomass production was enhanced by improved nutrient availability and diminished by illuminance.

Bacterial photophysiology was previously limited to photoautotrophs. The discovery of bacteriophytochromes in non-photoautotrophs raised a question whether these non-photoautotrophs are affected by the presence or absence of light? In this research work for the first time, bacterial hydrocarbon degradation and biomass production was studied under the influence of nutrients, illuminance (light flux) and time. An experimental model was designed, with six isolated bacterial strains (Pseudomonas poae BA1, Pseudomonas rhizosphaerae BP3, Bacillus thuringiensis BG3, Acinetobacter bouvetii BP18, Pseudomonas proteolytica BG31 and Stenotrophomonas rhizophila BG32) under four different conditions of nutrient media and illuminance at three time intervals of 15, 30, and 45days without shaking. All strains showed statistically higher hydrocarbon degradation under nutrient rich, dark conditions. Highest biodegradation (80.8, 79.4, and 78.7mg) was observed in BG31, BG17 and BG3 respectively. Nutrient rich media along with dark conditions improved the biomass production, and when media was nutrient deprived, higher biomass was produced in the presence of light. This work proved that light and nutrients significantly affect bacterial populations and hydrocarbon degradation. The optimal use of these parameters could facilitate to achieve the goal of remediation of hydrocarbon contaminated sites.

We consider the problem of multivariate multi-objective allocation where no or limited information is available within the stratum variance. Results show that a game theoretic approach (based on weighted goal programming) can be applied to sample size allocation problems. We use simulation technique to determine payoff matrix and to solve a minimax game.

Genetic algorithm’s performance is based on three major factors, which are selection criteria, crossover and mutation operators. Each factor has its own significant role but the selection criteria to choose parents from the population is the key role to running the genetic algorithm. There is a number of selection schemes that have been introduced in literature and all have their own advantages. Most of the selection criterion is chose the parents which give highly optimum values based on the theory that healthy parents produce healthy offspring. In the current study, we proposed a new selection scheme which selects healthy parents as well as unhealthy parents. The novel selection scheme is simple to implement, and it has notable ability to reduce the effected of premature convergence compared to other selection schemes. We apply this new technique along with some traditional selection schemes on six benchmark problems and Simulation studies show a remarkable performance of the proposed selection scheme.