This paper covers the Ant Colony OPTIMISATION Algorithm (ACO) as an OPTIMISATION method and discusses and recommends the utilization of the model in design and analysing of varies parameters related in the oil and gas pipeline network systems. This is to achieve the optimum length of the pipeline, pressure and flow rate. Ant Colony OPTIMISATION Algorithm (ACO) is capable of finding minimum path between several paths with their limitations and decreases pipe lengths from the sources to their destinations. It can be used in petroleum and gas refineries, transmissions and distributions lines. The theoretical and mathematical example of Ant Colony OPTIMISATION Algorithm (ACO) between two places were carried and calculated. Optimum length is calculated about100 cm with optimum pressure about 2440 psi and flow rate about 83*10 6 3 𝑚 𝑚 /ℎ . An example was designed based on the random variables results from 10 to 96 km for MATLAB Software and 0. 1 to 1 km for ANTCOL Software as Stochastic Variables (SV) of length (Km) between 14 stages which may show as „ SV-Matrix = Randi ([10 96], 14)‟ and Randi ([0. 1 1. 5], 14)” . For each iterations the SV matrixes are showed varies range of integrity. An example was assumed between 14 places with varies range of limitations which will be occurred during a pipeline project (FIG. 6 as an initially supposition graph) to find minimum path between stages to conclude optimum range of pressure and flow rate of oil and gases based on the optimum minimum paths of pipeline network systems. SV matrixes are used based on the MATLAB Code and ANTCOL software by the CPU core 2 Duo “ Intel” based on the ACO algorithm formulas. The output lines, graphs and diagrams of ACO algorithm are showed the minimum optimum path between 14 stages about 526 km with start point from station 6 and optimum flow-rate 0. 09810 6 𝑚 𝑚 3 /ℎ 𝑟 𝑟 and pressure drop about 714. 638 bar while 3. 631 km as minimum length with optimum flow rate 1. 515480*10 6 𝑚 𝑚 3 /ℎ 𝑟 𝑟 and pressure drop about 1192. 83 bar are found by ANTCAL results. The results proved the ability of ACO algorithm to find the optimum path with its effects on the other importance parameters, especially in the pipeline network systems, distribution and transmission lines and refineries.

An efficient formulation for the robust shape optimization of aerodynamic objects is ‎introduced in this paper. The formulation has three essential features. First, an Euler ‎solver based on a second-order Godunov scheme is used for the flow calculations. ‎Second, a genetic algorithm with binary number encoding is implemented for the ‎optimization procedure. The third ingredient of the procedure is the use of previous ‎flow that is closest in terms of geometric parameters to the new shape as an initial ‎condition for the new function evaluation. This makes the solution towards the final ‎value progressively faster and reduces the computer time for the convergence of the ‎algorithm. The algorithm is used to optimize two different problems, a simple bump ‎problem, and a two-dimensional transonic airfoil problem using an Euler solver solver ‎equation. The results indicate that GA/flow algorithm is robust and can optimize a ‎wide range of problems with a minimum implementation effort.

So far a large number of algorithms have been developed for OPTIMISATION of ultimate pit limits, most of which follow deterministic rules. In this paper a new algorithm is introduced, which follows a probabilistic logic using Markov chains. The algorithm is implemented on a transition matrix that correspond the 2D conventional economic block model of the orebody. Probability of mining a block is proportional to the profit it may produce. Applying this algorithm, probability of mining each block is obtained and finally the optimum pit is defined as the pit, which provides with the highest probability of mining. A 2D analysis of the problem is discussed in this paper; however, it is not difficult to expand the problem to 3D cases.

The sewer layout in flat areas significantly influences the construction and operational costs as well as reliability of the network performance. To find an optimum design of sewer networks for flat areas, this study presents a MULTI-OBJECTIVE optimization problem with the objective functions of 1- the cost and 2- the reliability. The reliability criterion is defined as the effect of a clogging in a sewer on its upstream population. To solve the problem, the NSGA-II Algorithm is coupled to two self-adaptive design algorithms for generating the layout and sizing of sewers. A case study is solved using the proposed model to demonstrate its application and advantages.

A MULTI-OBJECTIVE heuristic particle swarm optimiser (MOHPSO) based on Pareto multiobjective theory is proposed to solve MULTI-OBJECTIVE optimality problems. The optimality objectives are the roof displacement and structure weight. Two types of structure are analysed in this paper, a truss structure and a framework structure. Performance-based seismic analysis, such as classical and modal pushover analysis, is carried out for the structures. Four optimality algorithms, namely, NSGA-II, MOPSO, MGSO, and MOHPSO, were used for structural OPTIMISATION to compare the effectiveness of the algorithms. The calculation results indicate that MOHPSO outperformed the other algorithms in terms of solution stability, universality, and consistency of the distribution of the Pareto front and the ability to consider constraints. The population can converge to the true Pareto front in the latter generations, which indicates that MOHPSO is effective for engineering multiobjective optimality problems.