Improved Strength Pareto Global Adaptive Multi-Objective Optimization Search and Selection Algorithm (ISPGAEA)

Meta heuristic hybrid search optimization methods combine advantages of several methods in hybrid one. randomly generated solutions are archived, assessing diversity and convergence. However, after several cycles, random generated numbers tend to cluster rather than fill the search space. Uniform random number generators for initializing meta heuristic algorithms have drawbacks like uneven distribution, high disparities, and ineffective search space coverage. Control parameters in Genetic Algorithms, such as crossover and mutation, can impact the trade-off solutions of search space. The research introduces a hybrid interactive optimization approach, utilizing local enumerative search, dominance-based selection, and interactive indicator-based cross-over and mutation criteria to produce diverse, convergent trade-off solutions, set of benchmark problems are tested by the new method compared to a the famous SPEA2, the results show a better result for the new method ISPGAEA over SPEA2.