Abstract
Enterprise investment decision-making should not only consider investment profits, but also investment risks, which is a complex nonlinear multi-objective optimization problem. However, traditional investment decisions often only consider profit as a goal, resulting in an incorrect decision. Facing the high complexity of investment decision-making space, traditional multi-objective optimization methods pay too much attention to global search ability because of pursuing convergence speed and avoiding falling into local optimum, while local search ability is insufficient, which makes it difficult to converge to the Pareto optimal boundary. To solve this problem, an improved SPEA2 algorithm is proposed to optimize the multi-objective decision-making of investment. In the improved method, an external archive set is set up separately for local search after genetic operation, which guarantees the global search ability and also has strong local search ability. At the same time, the new crossover operator and individual update strategy are used to further improve the convergence ability of the algorithm while maintaining a strong diversity of the population. The experimental results show that the improved method can converge to the Pareto optimal boundary and improve the convergence speed, which can effectively realize the multi-objective decision-making of investment.
Highlights
Enterprise investment planning is constrained by many conditions
This paper proposes an improved SPEA2 algorithm based on local search for multi-objective decision-making of enterprise investment
The purpose of this paper is to propose a new local search algorithm to solve the problem that multi-objective investment optimization is easy to fall into local optimum
Summary
Enterprise investment planning is constrained by many conditions. In addition to the limitation of investment budget, there are some complicated economic constraints, which are mutually restrictive [1]. The existing decision-making methods of production and investment projects in enterprises mainly include the net present value (NPV) method [2], payback period method [3], cost–benefit analysis [4], Monte Carlo simulation approach [5], probability tree method [6], dynamic programming method [7], shortest path method [8,9], incremental effect evaluation method [10] and so on These methods have their own advantages and are widely used, whereas they have shortcomings, which can not meet the criteria of reasonable and scientific evaluation [11]. The decision-making methods mentioned above regarding production investment of enterprises mostly focus on optimization in terms of economic benefits of projects, while the investment of production projects of enterprises
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