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In this research article, we propose a new structure namely inverse left almost semigroup (LA-semigroup) by adding confusion in our proposed image encryption scheme along with discrete and continuous chaotic systems in order to complement the diffusion characteristics. The performance analysis was conducted in terms of the correlation analysis, the pixels uniformity analysis, the sensitivity analysis, the information entropy analysis, and pixels difference based measurements. The results show that the proposed algorithm has better information security properties, which can provide strong security and a high performance.

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The classical 0-1 knapsack problem is one of the more studied combinatorial optimization problem which belong to the NP class of algorithms. A number of its generalized forms have been addressed by various researchers using different designing techniques. In this paper, we design and analyze the Multiple Knapsack Problems (MKP) by using genetic algorithms. A modified Genetic Algorithm (mGA) is developed with the key focus on efficient encoding scheme for binary string representation and a competent dynamic programming based method for initial population generation. Furthermore transposition is applied in mGA instead of crossover for maintaining the population diversity. Performance analysis of the mGA, justifies our claims that the population incorporates adequate quality and diversity to reach a near optimal solution and transposition reduces the overall computation time.

The Evolutionary Computation has grown much in last few years. Inspired by biological evolution, this field is used to solve NP-hard optimization problems to come up with best solution. TSP is most popular and complex problem used to evaluate different algorithms. In this paper, we have conducted a comparative analysis between NSGA-II, NSGA-III, SPEA-2, MOEA/D and VEGA to find out which algorithm best suited for MOTSP problems. The results reveal that the MOEA/D performed better than other three algorithms in terms of more hypervolume, lower value of generational distance (GD), inverse generational distance (IGD) and adaptive epsilon. On the other hand, MOEA-D took more time than rest of the algorithms.

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