# IDEINFO: An Improved Vector-Weighted Optimization Algorithm
###### Abstract
In general, evolutionary algorithms can be divided into two types: single-solution-based and population-based algorithms [27]. In the first case, the search process of the algorithm starts with a single solution and updates its position during the optimization process. The best-known single-solution-based algorithms include single-solution-based simulation algorithms, including simulated annealing (SA) [13]. However, their drawbacks are the plausibility of high positions captured in the local optimum and the failure of information exchange, as these methods have only a single trend in the opposite direction. GA, DE, PSO, Ant Colony Optimization (ACO) [28], Artificial Bee Colony (ABC) [29], Harris Hawkeye Optimization (HHO) [30], Hunger Games Search (HGS) [31], Rungakuta Optimizer (RUN) [32], Sticky Fungus Algorithm (SMA) [33], and Whale Optimization (WOA) [34] are some examples of population-based algorithms. These methods can eliminate local optimization because they use a set of solutions in the optimization process. In addition, information exchange can be shared between solutions, which helps them to search better in difficult search spaces. However, these algorithms require significant computational costs for function evaluation and high-dimensional computation during optimization. Based on the above discussion, population-based algorithms are considered more reliable and robust optimization methods than single-solution-based algorithms. In general, the best formulation of the algorithm is investigated by evaluating different types of benchmark and engineering problems. Typically, the optimizer uses one or more operators to perform two phases: exploration and exploitation. An optimization algorithm requires a search mechanism to find promising regions in the search space, which is done in the exploration phase. The exploitation phase improves the local search capability and the speed of convergence to promising regions. Balancing these two phases is a challenging problem for any optimization algorithm. According to previous studies, no precise rules have been established so far to distinguish the most appropriate transition time from the exploration to the development phase, or the stochastic nature of this type of optimizer, due to its unexplored form [35]. Therefore, addressing this problem is crucial for developing and designing a stable and reliable optimization algorithm. Concerning the main challenges, in order to create a high-performance optimization algorithm, we focused on vector-weighted optimization algorithms with efficient optimization performance, which is based on the principle of vector-weighted averaging. By avoiding nature-inspired thinking, INFO can provide a promising way to avoid and reduce the challenges of other optimization algorithms, thus taking a step forward in having strong optimization capabilities for practical problems in the field of complex unknown search. In this paper, we report on our improvements to the INFO algorithm, which provides the following contributions:  
* A two-stage backward learning strategy that initializes candidate solutions, resulting in improved distribution uniformity and enhanced search capability. * A DE strategy that perturbs vector individuals to iteratively generate candidate solutions via greedy selection, which eliminates poorly adapted vectors and improves the local search ability. * A combined \(t\)-distribution and probabilistic strategy that expands the search range and avoids local optima traps. To evaluate our algorithm's performance, 14 sets of test functions are applied, improvement metrics are tested separately, and we compare our improved INFO model with the SSA, GWO, and baseline INFO algorithms.