The Differential Evolution Algorithm for Solving the Problem of Size Selection and Location of Infectious Waste Incinerator

Abstract

The disposal of infectious waste remains one of the most severe medical, social, and environmental problems in almost every country. Choosing the right location and arranging the most suitable transport route is one of the main issues in managing hazardous waste. Identifying a site for the disposal of infectious waste is a complicated process because both tangible and intangible factors must be considered together, and it also depends on various rules and regulations. This research aims to solve the problem of the size selection and location of infectious waste incinerators for 109 community hospitals in the upper part of northeastern Thailand by applying a differential evolution algorithm to solve the problem with the objective of minimizing the total system cost, which consists of the cost of transporting infectious waste, the fixed costs, and the variable cost of operating the infectious waste incinerator. The developed differential evolution produces vectors that differ from the conventional differential evolution. Instead of a single set of vectors, three are created to search for the solution. In addition to solving the problem of the case study, this research conducts numerical experiments with randomly generated data to measure the performance of the differential evolution algorithm. The results show that the proposed algorithm efficiently solves the problem and can find the global optimal solution for the problem studied.

Improvement in Sizing Constrained Analog IC via Ts-CPD Algorithm

“…The microchannel lengths of all MOS transistors have been set to a fixed value, L 1 = L 2 • • • L 8 = 0.8 µm, while voltage sources are set to V dd = −V ss = 2.5 V. Here, C c and I bias are independent variables. Thus, our algorithm determines its values in concordance with (19) and (20), respectively.…”

“…It is clear from Equations ( 15), ( 14) and ( 17) that, for the purpose of design, W 2 , W 4 and W 6 can be handled as independent variables, while W 1 , W 3 and W 5 as can be handled as dependent ones. W 7 is deduced from (18), thus, W 7 is also a dependent variable; I BI AS and C C are considered independent variables whose values are bounded by (19) and (20), respectively. Therefore, this example has 5 independent variables, W 2 , W 4 , W 6 , I BI AS and C c , whose values are selected by our algorithm and 5 dependent variables W 1 , W 3 , W 5 , W 7 and W 8 , whose impact over cost function and restrictions is evaluated by our algorithm to determine new values for independent variables, in an iterative process.…”

“…Specs. Ts-CPD MOL [62] SOA [63] PSO [22] HS [45] DE [45] ABC [45] GA [44] Load capacitance (pF) ≥2 The values of the bias current I BI AS are bounded by (20) and properly selected by our algorithm. For design, we considered R 1 as an independent variable.…”

“…More recently, evolutionary algorithms have been successfully applied to component value selection for analog active filters [18,19], facility location problem [20], truss structures [21], and to the analog integrated circuits design as in [22], where the sizing is achieved using a Particle Swarm Optimization (PSO) algorithm implemented in MATLAB R2008a and the results verified at the end with SPICE. In [23], a CMOS differential amplifier and a two stages CMOS op-amp are optimized to occupy the minimal possible area by the circuits and to improve their performances using the gravitational search algorithm in combination with the particle swarm optimization (GSA-PSO).…”