An Improved Modular Hybrid Ant Colony Approach for Solving Traveling Salesman Problem

Our primary aim is to design a framework to solve the well knowntraveling salesman problem(TSP) using combined approach ofAnt Colony Optimization (ACO) and Genetic Algorithm (GA).Several solutions exists for the above problem using ACO or GAand even using a hybrid approach of ACO and GA. Ourframework gives the optimal solution for the above problem byusing the modular hybrid approach of ACO and GA along withheuristic approaches.We have incorporated GA, RemoveSharpand LocalOpt heuristic approaches in ACO module, hence eachiteration calls the GA and heuristics within ACO module whichresults in a higher amount of pheromone deposited in the optimalpath for global pheromone update. As a result the convergence isquicker and solution is optimal

A study on saline water intrusion and fresh water recharge relevant to coastal environment

The paper is based on experimental laboratory model study with relevant mathematical analysis followed by field investigation so as to understand the characteristics and flow pattern of saline water intrusion into natural porous medium followed by subsequent fresh water recharge

Nature-Inspired Cloud–Crowd Computing for Intelligent Transportation System

Nowadays, it is crucial to have effective road traffic signal timing, especially in an ideal traffic light cycle. This problem can be resolved with modern technologies such as artificial intelligence, cloud and crowd computing. We hereby present a functional model named Cloud–Crowd Computing-based Intelligent Transportation System (CCCITS). This model aims to organize traffic by changing the phase of traffic lights in real-time based on road conditions and incidental crowdsourcing sentiment. Crowd computing is responsible for fine-tuning the system with feedback. In contrast, the cloud is responsible for the computation, which can use AI to secure efficient and effective paths for users. As a result of its installation, traffic management becomes more efficient, and traffic lights change dynamically depending on the traffic volume at the junction. The cloud medium collects updates about mishaps through the crowd computing system and incorporates updates to refine the model. It is observed that nature-inspired algorithms are very useful in solving complex transportation problems and can deal with NP-hard situations efficiently. To establish the feasibility of CCCITS, the SUMO simulation environment was used with nature-inspired algorithms (NIA), namely, Particle Swarm Optimization (PSO), Ant Colony Optimization and Genetic Algorithm (GA), and found satisfactory results

A 0–1 knapsack problem-based approach for solving open-pit mining problem with type-2 fuzzy parameters

Open-pit mining has several non-deterministic polynomial-time hard (NP-hard) 0–1 knapsack problems. The complexities of these problems are also increased due to some uncertain input parameters. This paper proposed an innovative hybrid fuzzy logic and genetic algorithm-based approach for solving a critical open-pit mining problem. First, the uncertainty of this problem is incorporated within the type-2 fuzzy environment, where the critical value reduction method was used to defuzzify the objective value. Next, genetic algorithm was used to solve the optimisation problem iteratively using a special initial solution generator, unique mutation, refinement, and immigration operations. Some benchmark instances from KPLIB were solved to show the effectiveness of the proposed hybrid fuzzy type-2 and genetic algorithm approach. The benchmark results show the proposed method can generate optimum solutions. Finally, a few OPMP instances from MineLib were solved using the proposed technique to demonstrate the applicability of this research to actual cases under fuzziness. The case study results indicated that the proposed approach can effectively solve the open-pit optimisation problem and similar NP-hard knapsack problems

Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

Bag-of-Tasks (BoT) scheduling over cloud computing resources called Cloud Bag-of-Tasks Scheduling (CBS) problem, which is a well-known NP-hard optimization problem. Whale Optimization Algorithm (WOA) is an effective method for CBS problems, which still requires further improvement in exploration ability, solution diversity, convergence speed, and ensuring adequate exploration–exploitation tradeoff to produce superior scheduling solutions. In order to remove WOA limitations, a hybrid oppositional differential evolution-enabled WOA (called h-DEWOA) approach is introduced to tackle CBS problems to minimize workload makespan and energy consumption. The proposed h-DEWOA incorporates chaotic maps, opposition-based learning (OBL), differential evolution (DE), and a fitness-based balancing mechanism into the standard WOA method, resulting in enhanced exploration, faster convergence, and adequate exploration–exploitation tradeoff throughout the algorithm execution. Besides this, an efficient allocation heuristic is added to the h-DEWOA method to improve resource assignment. CEA-Curie and HPC2N real cloud workloads are used for performance evaluation of scheduling algorithms using the CloudSim simulator. Two series of experiments have been conducted for performance comparison: one with WOA-based heuristics and another with non-WOA-based metaheuristics. Experimental results of the first series of experiments reveal that the h-DEWOA approach results in makespan improvement in the range of 5.79–13.38% (for CEA-Curie workloads), 5.03–13.80% (for HPC2N workloads), and energy consumption in the range of 3.21–14.70% (for CEA-Curie workloads) and 10.84–19.30% (for HPC2N workloads) over well-known WOA-based metaheuristics. Similarly, h-DEWOA also resulted in significant performance in comparison with recent state-of-the-art non-WOA-based metaheuristics in the second series of experiments. Statistical tests and box plots also revealed the robustness of the proposed h-DEWOA algorithm

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Not AvailableTurmeric (Curcuma longa L.), the golden spice of India, offers a myriad of health benefits primarily due to the presence of polyphenolic curcuminoid pigments. It is believed that the dark orange colour of turmeric rhizome has wide-spread health benefits; but no reliable evidential proof for this is available till date. It is crucial to discern the association of rhizome colour with health benefits. It is also of prime importance to investigate whether the curcuminoids present in the rhizomes are the sole bioactive compounds contributing to its medicinal properties. In the present study, forty five turmeric genotypes were collected from the sub-Himalayan terai region of India which differed in their rhizome colour. Here, the Turmeric Redness Index (TRI) in terms of rhizome colour content (a */b *) of the genotypes revealed strong correlation with curcuminoid content ( + 0.93), total phenol content ( + 0.76), total flavonoids content ( + 0.82), iron content ( + 0.55) and antioxidant activity (–0.90 and –0.92 for DPPH and ABTS assays and + 0.82 and + 0.95 for TAC and CUPRAC assays, respectively, with p < 0.001). Among the studied genotypes, TCP 2 (Turmeric Collection Pundibari 2) having dark orange coloured rhizome turned out to be a superior genotype in terms of its antioxidant potential, curcuminoid and iron content. Quantification of phenolic and flavonoid compounds of TCP 2 revealed that other than the three isoforms of curcuminoids, different therapeutically important bioactive compounds like p-coumaric acid (162.46 mg/kg), catechin (107.67 mg/kg), sinapic acid (417.36 mg/kg) and flavonoid like quercetin (2746.21 mg/kg) are also present in the rhizome, suggesting that along with curcuminoid these compounds also contribute towards its antioxidant potential. The GC–MS analysis of the essential oil of TCP 2 revealed the presence of thirty three volatile compounds with significantly high ar-turmeron (28.57 %), curlone (10.05 %), eucalyptol (10.13 %) and chemigran (13 %). A rapid and improved micro-propagation protocol was also developed for TCP 2 using single rhizome bud sprout as explants. A media composition of MS with 6 ppm BAP and 2 ppm NAA was found to be the best for rapid and efficient micro shoot development.Not Availabl