Investigating Metaheuristics Applications for Capacitated Location Allocation Problem on Logistics Networks

This chapter describes and discusses stochastic optimization methods for solving problems involved in process intensification, given an emphasis in multiobjective optimization due to its increasing importance in the chemical engineering community. A brief description of the multiobjective optimization strategies such as genetic algorithms, simulated annealing, tabu search, differential evolution, ant colony and particle swarm optimization is provided, including several applications of evolutionary optimization methods in the intensification of separation processes.

Ant Colony Optimization (ACO) has been successfully employed to tackle a variety of hard combinatorial optimization problems, including the traveling salesman problem, vehicle routing, sequential ordering and timetabling. ACO, as a swarm intelligence framework, mimics the indirect communication strategy employed by real ants mediated by pheromone trails. Among the several algorithms following the ACO general framework, the Ant Colony System (ACS) has obtained convincing results in a range of problems. In Software Engineering, the effective application of ACO has been very narrow, being restricted to a few sparse problems. This paper expands this applicability, by adapting the ACS algorithm to solve the well-known Software Release Planning problem in the presence of dependent requirements. The evaluation of the proposed approach is performed over 72 synthetic datasets and considered, besides ACO, the Genetic Algorithm and Simulated Annealing. Results are consistent to show the ability of the proposed ACO algorithm to generate more accurate solutions to the Software Release Planning problem when compared to Genetic Algorithm and Simulated Annealing.

Considering deregulation in power systems and the energy conservation law, power and energy systems require more cost and energy reduction for system planning, operation, and control. Optimization techniques such as linear and nonlinear programming techniques have been utilized as one of the methods for realization of the reduction. Recently, meta-heuristics such as genetic algorithm, simulated annealing, tabu search, and particle swarm optimization have been paid attention as other options for realization of the reduction. In power and energy society, we had one technical committee and one special issue on applications of meta-heuristics for power system. Moreover, panel sessions and tutorials have been held in IEEE and IFAC. This paper presents applications of meta-heuristics to power and energy fields from the practical application point of view.

This Portfolio selection Problem (PSP) remains an intractable research problem in finance and economics and often regarded as NP-hard problem in optimization and computational intelligence. This paper solved the extended Markowitz mean- variance portfolio selection model with an efficient Metaheuristics method of Generalized Differential Evolution 3 (GDE3). The extended Markowitz mean- variance portfolio selection model consists of four constraints: bounds on holdings, cardinality, minimum transaction lots, and expert opinion. There is no research in literature that had ever engaged the set of four constraints with GDE3 to solve PSP. This paper is the first to conduct the study in this direction. The first three sets of constraints have been presented in other researches in literatures. This paper introduced expert opinion constraint to existing portfolio selection models and solved with GDE3. The computational results obtained in this research study show improved performance when compared with other Metaheuristics methods of Genetic algorithm (GA), Simulated Annealing (SA), Tabu Search (TS) and Particle Swarm Optimization (PSO).

In order to solve the optimization of new energy bus line network, a new energy bus line network based on ant colony algorithm and low-carbon concept is proposed. Firstly, the model of public transport network is established combined with OD matrix, and the improved ant colony algorithm is used to iteratively optimize the initial suboptimal route set, optimize the objective function, and finally obtain the optimal public transport route set. Secondly, the improved ant colony algorithm based on simulated annealing solves the design of public transport network, and the optimization scheme greatly reduces the number of passenger transfers and the total travel time of passengers. Finally, simulated annealing algorithm, basic ant colony algorithm, and simulated annealing improved ant colony algorithm are used to optimize the objective function. It is proved that the improved ant colony algorithm of simulated annealing can find the optimal solution of the three algorithms when solving the problem of public transport network design, which is better than the solution of basic ant colony and simulated annealing algorithm. The solution efficiency is 10.6 times that of simulated annealing algorithm and 3.5 times that of basic ant colony algorithm.

Hybrid meta-heuristic algorithms for solving network design problem

Ants communicate information by leaving pheromone tracks. A moving ant leaves, in varying quantities, some pheromone on the ground to mark its way. While an isolated ant moves essentially at random, an ant encountering a previously laid trail is able to detect it and decide with high probability to follow it, thus reinforcing the track with its own pheromone. The collective behavior that emerges is thus a positive feedback: where the more the ants following a track, the more attractive that track becomes for being followed; thus the probability with which an ant chooses a path increases with the number of ants that previously chose the same path. This elementary ant's behavior inspired the development of ant colony optimization by Marco Dorigo in 1992, constructing a meta-heuristic stochastic combinatorial computational methodology belonging to a family of related meta-heuristic methods such as simulated annealing, Tabu search and genetic algorithms. This book covers in twenty chapters state of the art methods and applications of utilizing ant colony optimization algorithms. New methods and theory such as multi colony ant algorithm based upon a new pheromone arithmetic crossover and a repulsive operator, new findings on ant colony convergence, and a diversity of engineering and science applications from transportation, water resources, electrical and computer science disciplines are presented.

The research and development of metaheuristic methods are critical issues in computer science. In the past decade, metaheuristic algorithms have been used in many engineering applications such as optimization of engineering problems, telecommunications, information security, and image processing. Many metaheuristic algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) are recently becoming very popular.There are many studies conducted in the literature on the comparison of PSO with other metaheuristic algorithms. In this chapter, various studies carried out between the years of 2010 and 2020 about the comparison of PSO with the other metaheuristic algorithms will be examined. The metaheuristic algorithms to be considered are simulated annealing (SA), genetic algorithm (GA), differential evolution (DE), ant colony optimization (ACO), artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), tabu search (TS), harmony search (HS), firefly algorithm (FF), cuckoo search (CS), bat-inspired algorithm (BA), water wave optimization (WWO), clonal selection algorithm (CLONALG), chemical reaction optimization (CRO), sine cosine algorithm (SCA), glowworm swarm optimization (GSO), and grey wolf optimizer (GWO). This study aims to evaluate and analyze the covered papers according to several criteria such as (a) rates of studies according to publishing years, (b) the metaheuristic algorithms that are compared to PSO, (c) performance evaluation of compared algorithms, (d) the metaheuristic algorithms with their inspirational approaches and their initial proposed studies and years, (e) the field of subjects where the algorithms are applied in the reviewed studies, and (f) used databases in the examined studies.This study is a comprehensive literature review of the comparison of PSO with the most popular metaheuristic algorithms. The intention of this review is to be useful for researchers who want to conduct a survey on this area of the subject as this chapter will cover the essential and helpful analysis of the related research.

Examining the economics of multi-pass machining operations has significant practical importance. Traditional optimization techniques have been used, but are limited in application. Non-traditional optimization techniques like genetic algorithms, simulated Annealing and ant colony optimization are increasingly used to solve optimization problems. This paper discusses the use of non-traditional optimization techniques for optimizing the depth of cut in multi-pass turning. The resulting subdivision of the cut depth indicates the proposed methodologies are competent, efficient and accurate.

In this paper, an effective ant lion optimisation (ALO) algorithm is proposed for two stage supply chain network associated with fixed charge transportation problem (FCTP) which is strongly NP-hard. In a FCTP, fixed cost is incurred for every route, along with the variable cost that is proportional to the amount shipped. In some circumstances, the variable cost is associated with quadratic variables in which the cost function will be nonlinear. The aim of this paper is to determine the least cost transportation plan that minimises the total variable and fixed costs while satisfying the supply and demand requirements of each plant and customer. The performance of the proposed ALO is compared in terms of total cost with other algorithms addressed in the literature such as genetic algorithm, simulated annealing and ant colony optimisation. The computational results reveal that the proposed ALO provides better solutions.

Aiming at the disadvantage of premature convergence of basic genetic algorithm, an adaptive simulated annealing genetic tabu search algorithm is proposed. This algorithm fully combines the global convergence and adaptability of simulated annealing algorithm and the strong climbing ability and high efficiency of tabu search strategy. It has strong convergence and adaptability. The simulation results of the adaptive simulated annealing genetic tabu search algorithm are given and compared with the basic genetic algorithm and simulated annealing algorithm. The simulation results show that the algorithm has better convergence and optimization performance, and can better solve the combinatorial optimization problem.

This chapter presents a comparison of population-based (e.g. Genetic Algorithms (GA), Firefly Algorithm (FA), and Ant Colony Optimization (ACO))and single point search meta-heuristic methods (e.g. Simulated Annealing (SA), Threshold Accepting (TA), and Tabu Search (TS)) applied to an optimal tuning of a universal digital proportional-integral-derivative (PID) controller. The PID controllers control feed rate and maintain glucose concentration at the desired set point for an E. coli MC4110 fed-batch cultivation process. The model of the cultivation process is represented by dynamic non-linear mass balance equations for biomass and substrate. In the control the design measurement, process noise, and time delay of the glucose measurement system were taken into account. To achieve good closed-loop system the constants (Kp, Ti, Td, b, c and N) were tuned in the PID controller algorithm so the controller can provide control action designed for the specific process requirements resulting in an optimal set of PID controller settings. For a time the controllers set and maintained the control variable at the desired set point during the E. coli MC4110 fed-batch cultivation process. Average, best, and worst objective function values and PID controller's parameters were used as criteria to compare the performance of the considered meta-heuristic algorithms. This indicates that the population-based meta-heuristics performs better than the single point search methods. GA and ACO show better performance than FA. It also indicates that TS results are comparable to those of FA. The results show that SA and TA algorithms failed to solve the PID controller tuning problem.

This study introduces the Spanning-Tree Matrix Approach, a novel computational method for modeling large-scale, tree-shaped water distribution networks. The method focuses on minimizing network cost while satisfying hydraulic design constraints. A case study demonstrated the integration of this approach with the Honey-Bee Mating Optimization algorithm to determine the optimal combination of pipe diameters. Results confirm the model’s effectiveness and scalability for tree-shaped network configurations. The Spanning-Tree Matrix Approach is also versatile—it can accommodate various governing equations and design criteria, and can be easily embedded in modern stochastic optimization algorithms such as Genetic Algorithms, Simulated Annealing, and Ant-Colony Optimization. Due to its adaptability and simplicity, further research is encouraged to apply this approach to other spanning-tree hydraulic systems, including waster water networks, river or groundwater networks.

A systems-based approach for generating quantitative models of microstructural evolution in silicon materials processing

Alternative material flow strategies in logistics networks have crucial influences on the overall performance of the networks. Material flows can follow push, pull, or hybrid systems. To get the advantages of both push and pull flows in networks, the decoupling-point strategy is used as coordination mean. At this point, material pull has to get optimized concerning customer orders against pushed replenishment-rates. To compensate the ambiguity and uncertainty of both dynamic flows, fuzzy set theory can practically be applied. This paper has conceptual and mathematical parts to explain the performance of the push-pull flow strategy in a supply network and to give a novel solution for optimizing the pull side employing Conwip system. Alternative numbers of pallets and their lot-sizes circulating in the assembly system are getting optimized in accordance with a multi-objective problem; employing a hybrid approach out of meta-heuristics (genetic algorithm and simulated annealing) and fuzzy system. Two main fuzzy sets as triangular and trapezoidal are applied in this technique for estimating ill-defined waiting times. The configured technique leads to smoother flows between push and pull sides in complex networks. A discrete-event simulation model is developed to analyze this thesis in an exemplary logistics network with dynamics.