Solving Large 0–1 Multidimensional Knapsack Problems by a New Simplified Binary Artificial Fish Swarm Algorithm

A Fish Swarm Algorithm for Financial Risk Early Warning

The financial risk early warning system is important for promoting the sustainable development of enterprise, and in this article, the fish swarm algorithm is applied to it. First, the main financial risk factors of enterprise are summarized. Second, the index system of the financial risk early warning system is constructed based on relating theory. Third, the basic theory of artificial fish swarm algorithm is studied, and the mathematical models are constructed. Then, the wavelet neutral network is improved based on the fish swarm algorithm, and the algorithm procedure is designed. Finally, a simulation analysis is carried out, and the predicting correctness of samples is 100%, and results show that the fish swarm algorithm is an effective method for improving the financial risk early warning system.

A Multi-Agent Based Optimization Method for Combinatorial Optimization Problems

This paper introduces a Multi-Agent based Optimization Method for Combinatorial Optimization Problems named MAOM-COP. In this method, a set of agents are cooperatively interacting to select the appropriate operators of metaheuristics using learning techniques. MAOM-COP is a flexible architecture, whose objective is to produce more generally applicable search methodologies. In this paper, the MAOM-COP explores genetic algorithm and local search metaheuristics. Using these metaheuristics, the decision-maker agent, the intensification agents and the diversification agents are seeking to improve the search. The diversification agents can be divided into the perturbation agent and the crossover agents. The decision-maker agent decides dynamically which agent to activate between intensification agents and crossover agents within reinforcement learning. If the intensification agents are activated, they apply local search algorithms. During their searches, they can exchange information, as they can trigger the perturbation agent. If the crossover agents are activated, they perform recombination operations. We applied the MAOM-COP to the following problems: quadratic assignment, graph coloring, winner determination and multidimensional knapsack. MAOMCOP shows competitive performances compared with the approaches of the literature.