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Meta heuristic algorithms are very important methods, they used mainly in solving combinatorial optimization problems. They are stochastic in manner and simulate the behavior of any population of particles. Meta heuristics algorithms try to find optimal or near to optimal solution when solving complex optimization problem. In this paper two modified emperor penguins colony optimization algorithms MEPC1 and MEPC2 were developed. Original Emperor Penguins Colony (EPC) algorithm simulates the behavior of emperor of penguins, two modifications are done to the original EPC, Archimedes and hyperbolic spiral like movement are used instead of logarithmic spiral like movement. The two modified algorithms are compared with the original algorithm through ten test functions; the results show that the modified algorithms are achieved better than the original one. [ABSTRACT FROM AUTHOR]

Sustainability is an important consideration in product design. The sustainable design should fully consider the environmental, social, and economic factors of the product. However, the three factors are often conflicting with each other. This paper aims to strike a balance between these factors and achieve sustainable product design through multi-objective optimization. The three influencing factors of sustainability, namely, the environmental factor, social factor and economic factor, were respectively defined as environmental impact, labor time and labor cost. Then, the product to be designed was represented as a design structure matrix (DSM), a list of all product components and the dependency patterns among these components. On this basis, the non-dominated sorting and cuckoo search were combined into a multi-objective optimization technique to optimize the product functionality. This technique looks for a set of Pareto optimal solutions, each of which represents the structure of modules and the number of modules. The effectiveness of the proposed technique was verified through a case study on a coffee maker. The results show that our technique outperformed the previous optimization methods. [ABSTRACT FROM AUTHOR]