Simulation-based Particle Swarm Optimization Research Articles

A novel multi-agent simulation based particle swarm optimization algorithm.

Recently, there has been considerable research on combining multi-agent simulation and particle swarm optimization in practice. However, most existing studies are limited to specific engineering fields or problems without summarizing a general and universal combination framework. Moreover, particle swarm optimization can be less effective in complex problems due to its weakness in balancing exploration and exploitation. Yet, it is not common to combine multi-agent simulation with improved versions of the algorithm. Therefore, this paper proposes an improved particle swarm optimization algorithm, introducing a multi-level structure and a competition mechanism to enhance exploration while balancing exploitation. The performance of the algorithm is tested by a set of comparison experiments. The results have verified its capability of converging to high-quality solutions at a fast rate while holding the swarm diversity. Further, a problem-independent simulation-optimization approach is proposed, which integrates the improved algorithm into multi-agent systems, aiming to simulate realistic scenarios dynamically and solve related optimization problems simultaneously. The approach is implemented in a response planning system to find optimal arrangements for response operations after the Sanchi oil spill accident. Results of the case study suggest that compared with the commonly-used shortest distance selection method, the proposed approach significantly shortens the overall response time, improves response efficiency, and mitigates environmental pollution.

High-order Markov-switching portfolio selection with capital gain tax

The uncertainties of market state and returns of risky assets both affect the investors’ decisions significantly. It is necessary and prudent to consider the regime-switching mechanism of market states in portfolio selection. Different from the traditional first-order Markov-switching portfolio selection studies, we consider a high-order Markov transition process of market state, which can better depict the market state changes and incorporate more market information into portfolio selection due to the financial market has the long memory property. The capital gain tax is treated as the trading cost of which the tax rate not only depends on the holding periods of risky assets but also on the trading volume. In addition, the capital gain–loss offsetting is studied explicitly where the gain–loss offsetting in the same period and capital loss carry-over effect in different periods are considered simultaneously. A high-order Markov-switching portfolio selection model (HOMSPSM) is proposed. The Monte Carlo simulation is employed to approximate the expected values and variances of the complicated random returns, and the Monte Carlo simulation based particle swarm optimization algorithm (MCPSO) is designed to obtain the optimal investment strategy. Finally, simulated and practical numerical experiments are provided to verify the effectiveness and practicability of HOMSPSM and MCPSO.

Simulation-based particle swarm optimization technique for the breadth design of stairs and passageway network at interchange stations

This study reports a novel hybrid simulation based Particle Swarm Optimization (PSO) technique using PH-Distribution for the design of facilities at transit stations. First, a Discrete-Event Simulation (DES) model for the network of stairs and passageway is developed that incorporates all influential factors that were ignored by the existing design codes (TCRP Report-165). The DES model is coupled with the PSO technique to build an automated framework for the area of network of stairs and passageway. The experimental results by hybrid simulationoptimization reveal that: (1) increase in randomness (SCV) of passengers’ arrival causes increase in the breadth of both passageways and stairs; 2) For the same arrival rate and SCV, the stairs breadths are more than passageway breadths; 3) The breadth decreases from Level of Service (LOS) B towards D.

A fuzzy random EPQ model for imperfect quality items with possibility and necessity constraints

This paper investigates a single-period production-inventory model in linguistic ‘imprecise’ and statistical ‘uncertain’ environment, where fuzziness and randomness appear simultaneously. In order to capture the stochastic variability of demand uncertainty, we consider it as a random variable. We also model the hazards of imperfect quality production due to the production process shifting from “in-control” state to “out-of-control” state. The process shifting time and fraction of imperfect quality items are characterized as fuzzy random variables (FRVs). The model restricts the budget and allowable shortages. To mitigate the effect of uncertain demand, fluctuating cost parameters, etc., the imposed constraints are considered in fuzzy sense. To model the above scenarios, we formulate a fuzzy mathematical programming problem. By employing the fuzzy expectation, signed distance, and possibility/necessity measure, the fuzzy model is transformed into an equivalent deterministic non-linear programming problem. A fuzzy simulation based particle swarm optimization (PSO) algorithm is also employed to the model. The effectiveness of the model and solution methodologies are demonstrated with the help of numerical illustrations.