No abstract

Swarm intelligence algorithms (SI) have an excellent ability to search for the optimal solution and they are applying two mechanisms during the search. The first mechanism is exploration, to explore a vast area in the search space, and when they found a promising area they switch from the exploration to the exploitation mechanism. A good SI algorithm can balance the exploration and the exploitation mechanism. In this paper, we propose a modified version of the chimp optimization algorithm (ChOA) to train a feed-forward neural network (FNN). The proposed algorithm is called a modified weighted chimp optimization algorithm (MWChOA). The main drawback of the standard ChOA and the weighted chimp optimization algorithm (WChOA) is they can be trapped in local optima because most of the solutions update their positions based on the position of the four leader solutions in the population. In the proposed algorithm, we reduced the number of leader solutions from four to three, and we found that reducing the number of leader solutions enhances the search and increases the exploration phase in the proposed algorithm, and avoids trapping in local optima. We test the proposed algorithm on the Eleven dataset and compare it against 16 SI algorithms. The results show that the proposed algorithm can achieve success to train the FNN when compare to the other SI algorithms.

Team Formation (TF) is considered one of the most significant problems in computer science and optimization. TF is defined as forming the best team of experts in a social network to complete a task with least cost. Many real-world problems, such as task assignment, vehicle routing, nurse scheduling, resource allocation, and airline crew scheduling, are based on the TF problem. TF has been shown to be a Nondeterministic Polynomial time (NP) problem, and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms. This paper proposes two improved swarm-based algorithms for solving team formation problem. The first algorithm, entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm (HBOSA), uses a single crossover operator to improve the performance of a standard heap-based optimizer (HBO) algorithm. It also employs the simulated annealing (SA) approach to improve model convergence and avoid local minima trapping. The second algorithm is the Chaotic Heap-based Optimizer Algorithm (CHBO). CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space. During HBO's optimization process, a logistic chaotic map is used. The performance of the two proposed algorithms (HBOSA) and (CHBO) is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills. Furthermore, the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer (HBO), Developed Simulated Annealing (DSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Genetic Algorithm (GA). Finally, the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database (IMDB). The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance, with fast convergence to the global minimum.