Evolutionary multi-objective optimization algorithms are generally employed to generate Pareto optimal solutions by exploring the search space. To enhance the performance, exploration by global search can be complemented with exploitation by combining it with local search. In this paper, we address the issues in integrating local search with global search such as: how to select individuals for local search; how deep the local search is performed; how to combine multiple objectives into single objective for local search. We introduce a Preferential Local Search mechanism to fine tune the global optimal solutions further and an adaptive weight mechanism for combining multi-objectives together. These ideas have been integrated into NSGA-II to arrive at a new memetic algorithm for solving multi-objective optimization problems. The proposed algorithm has been applied on a set of constrained and unconstrained multi-objective benchmark test suite. The performance was analyzed by computing different metrics such as Generational distance, Spread, Max spread, and HyperVolume Ratio for the test suite functions. Statistical test applied on the results obtained suggests that the proposed algorithm outperforms the state-of-art multiobjective algorithms like NSGA-II and SPEA2. To study the performance of our algorithm on a real-world application, Economic Emission Load Dispatch was also taken up for validation. The performance was studied with the help of measures such as Hypervolume and Set Coverage Metrics. Experimental results substantiate that our algorithm has Communicated by V. Loia.

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Endeavours for adopting network data greatly from social media allude to give the specific methods in extracting value from data space, for example, conversion, transaction, message and others, where structured information sources originate from big business assets information and unstructured information sources originate from video and audio. It very well may be accomplished to extend the way toward extracting value from social network for designing the information sources to satisfy the association objective. This paper means to uncover the method for approach of big data in extracting information esteem from information complexity including velocity and variety into volume. This investigation was led employing contents analysis by looking into certain literary works in chapters, books and peer-audited journals and procedures in creating prototype employing data analytics related from users, time analytics and topic. The discoveries uncover that big data rising technology with analytics procedure gives specific favourable circumstances to change the trend of data fitted into innovative atmosphere of OLR i.e. online learning resources to upgrade in building up learning resources. Both model and prototype information extraction value can be upgraded to encourage the learning atmosphere in supporting the implementations with convenience and ease. This investigation is relied upon to add to improve the outcomes and learning atmosphere with achievement and performance by upgrading the learning process development of students to give online resources in the higher education setting.

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Problem statement: In the current modern business environment, its success is defined by collaboration, team efforts and partnership, rather than lonely spectacular individual efforts in isolation. So the collaboration becomes especially important because of the mutual benefit it brings. Sometimes, such collaboration even occurs among competitors, or among companies that have conflict of interests, but the collaborators are aware that the benefit brought by such collaboration will give them an advantage over other competitors. Approach: For this kind of collaboration, data's privacy becomes extremely important: all the parties of the collaboration promise to provide their private data to the collaboration, but neither of them wants each other or any third party to learn much about their private data. One of the major problems that accompany with the huge collection or repository of data is confidentiality. The need for privacy is sometimes due to law or can be motivated by business interests. Results: Performance of privacy preserving collaborative data using secure multiparty computation is evaluated with attack resistance rate measured in terms of time, number of session and participants and memory for privacy preservation. Conclusion: Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed, even to the party running the algorithm

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