Many real-world problems not only require the simultaneous optimization of a number of objective functions, but also need to track the changing optimal solutions. These problems are called: Dynamic multi-objective optimization. These optimization problems do not have a single goal to solve, but many goals that are in conflict with one anotherimprovement in one goal leads to deterioration of another. In dynamic multi-objective optimization problems (DMOOP) where either the objective functions or the constraints change over time, an optimization algorithm should be able to find, obtain and track the changing set of optimal solutions (POS) and the approximated Pareto front as close as true Pareto front (POF). In order to determine whether an algorithm can work efficiently in changing environments, it should be evaluated on standard benchmark functions. In addition, to measure the performance of the algorithm and compare it to other algorithms, performance metrics are required. This program aims at bringing academic researchers and practitioners together to review the concepts and definitions, algorithms and techniques, standard benchmark functions, performance measures and challenges of dynamic multi-objective optimization.