Green Project Planning with Realistic Multi-Objective Consideration in Developing Sustainable Port

The huge bandwidth of optical fibres is exploited through wavelength division multiplexing technology, which introduces new complexities in the routing problem. In this context, the wavelength converter allocation problem has become a key factor to minimize blocking. The wavelength converter allocation problem has been treated as a mono-objective problem minimizing the number of wavelength converters or minimizing blocking; however, both criteria are in conflict with each other. Therefore, the wavelength converter allocation problem is studied here in a pure multi-objective optimization context for more appropriate decision making. This work proposes a multi-objective optimization approach based on an evolutionary algorithm which simultaneously minimizes blocking and the number of wavelength converters. Extensive simulations on three real optical networks show promising results in the sense that our algorithm generates the trade-off curve between blocking and the number of converters needed, and outperforms a recently proposed approach.

This paper describes the development and application of a new multi-objective evolutionary optimization approach for the design and upgrading of water distribution systems with multiple pumps and service reservoirs. The optimization model employs a pressure-driven analysis simulator that accounts for the minimum node pressure constraints and conservation of mass and energy. Pump scheduling, tank siting and tank design are integrated seamlessly in the optimization without introducing additional heuristic procedures. The computational solution of the optimization problem is entirely penalty-free, thanks to pressure-driven analysis and the inclusion of explicit criteria for tank depletion and replenishment. The model was applied to the Anytown network that is a benchmark optimization problem. Many new solutions were achieved that are cheaper and offer superior performance compared to previous solutions in the literature. Detailed and extensive simulations of the solutions achieved were carried out. Spatial and temporal variations in water quality were investigated by simulating the chlorine residual and disinfection by-products in addition to water age. The hydraulic requirements were satisfied; efficiency of pumps was consistently high; effective operation of the new and existing tanks was achieved; water quality was improved; and overall computational efficiency was high. The formulation is entirely generic.

Controller design is performed to meet some performance criteria. Some of them are the gain and phase margins, control effort, settling time, overshoot, amongst other indexes. Obtaining a controller or compensator that fits all these criteria (or some of them) for a closed loop control is done in both empirical and analytical procedures. The most of these procedures assumes the plant to be controlled could be modelled as first or second-order system. This paper aims to show a way to find the controller parameters based on the multi-objective optimization with a evolutionary approach. The systems to be controlled must be the type SISO (Single Input Single Output) whose transfer functions may have order higher than second order.

Municipalities across the United States face the challenge of sanitary sewer overflows (SSOs), events which pose serious public health and environmental problems. SSOs are unintentional discharges of untreated sewage from the sewer system that can occur as a result of rain derived infiltration and inflow (RDII). SSOs can be reduced by decreasing RDII into the collection system, increasing conveyance capacity, and lessening peak flow through detention storage facilities. However, the sheer length of sanitary sewer systems makes the implementation of these controls very costly. Thus, a novel approach is required to a posteriori design cost-effective rehabilitation options. This study describes a multiobjective evolutionary optimization approach to design a rehabilitation strategy for SSOs reduction in the eastern subsewershed of the San Antonio Water System (SAWS) sewer network. The subsewershed consists of 3,304 conduits connected via 3,155 manholes to form a network that is 160.8 miles long and services an area covering 20.4 square miles with an approximate population of 36,000 inhabitants. The hydraulic behavior of the system is simulated using the EPA Storm Water Management Model (EPA-SWMM) hydraulic model, and the number of SSOs is calculated as the number of nodes that oveflow during the 5 year–6 hours design storm. The optimization method utilized was the Nondominated Sorting Genetic Algorithm (NSGA II) to generate near Pareto-optimal solutions that express tradeoffs between the number of SSOs and cost. The NSGA II was implemented in JAVA, where each individual is represented as a set of vectors that is composed of three sets of genes. The decision variables include the number of pipe segments to be replaced, the location of the first link to be replaced, and the amount of commercial diameter increase from existing pipe diameter. The use of the optimization approach is expected to generate more efficient solutions in comparison to typical engineering approaches that look into solving a localized SSOs problems in sanitary sewer networks.

Sensitivity analysis to precursor emissions of multi-objective air quality control policies

This paper presents an application of a constrained multiobjective evolutionary algorithm for the design of active suspension controllers for light rail vehicles with the aim of providing superior ride comfort within the suspension's stroke limitation. A multibody dynamic model of a three‐car train is derived and the control parameters are optimized. Force cancellation, skyhook damper, and track‐following are used to synthesize the active controller. Selection of the active suspension parameters is aided by an evolutionary computation algorithm to get the best compromise between ride quality and suspension deflections due to irregular gradient tracks. An evolutionary multiobjective optimization approach accompanied with the Pareto set is proposed to deal with the complicated control design problem.

In this paper, we present a surrogate-based multi-objective evolutionary optimization approach to optimize airfoil aerodynamic designs. Our approach makes use of multiple surrogate models which operate in parallel with the aim of combining their features when solving a costly multi-objective optimization problem. The proposed approach is used to solve five multiobjective airfoil aerodynamic optimization problems. We compare the performance of a multi-objective evolutionary algorithm with surrogates with respect to the same approach without using surrogates. Our preliminary results indicate that our proposal can achieve a substantial reduction in the number of objective function evaluations, which has obvious advantages for dealing with expensive objective functions such as those involved in aeronautical optimization problems.

Exposure to noise is a significant problem for communities that exist near airports. The distribution of noise exposure can be positively affected by changes in the procedures that aircraft follow in the vicinity of an airport (e.g. rate of ascent, ground track, etc.). When considering such changes, a decision maker often has to weigh the objective of lower noise impact against 'more practical' considerations such as fuel consumption and time-of-flight. This study presents a method of numerical optimization which seeks to find the optimal-tradeoff set (Pareto front) of flight procedures given information about an airport and the surrounding population and geography. This front will only include procedures such that an aggregate noise metric cannot be improved without detriment to a more practical objective. A contemporary multi-objective evolutionary algorithm is used as the basis of the optimization effort. Results from a simulated military airfield near Asheville, NC are shown. Ways in which decision makers are empowered by having access to a Pareto front are discussed.

This article examines China's Belt and Road Initiative (BRI) project through the green development discourse and green financing policies. Under the BRI, China disseminated 183 billion United States dollars (USD) as energy finance for BRI countries to achieve the capacity building to achieve the Sustainable Development Goals (SDGs) of 2030. I attempt to critically analyse how China promotes its eco-friendly infrastructure development policies through the BRI projects. The first question is why China construct ecologically smart and sustainable development projects under the ambitious ongoing BRI. And secondly, how China is creating comprehensive strategies and policy coordination for implementing green infrastructure development projects in BRI countries. Moreover, this paper argues whether the 'Chinese model' of green development is successful and why it encounters criticism in terms of environmental records, carbon neutrality and conservation methods.

Mechanical and hydrophobic properties of chromium carbide films via a multi-objective optimization approach

Optimization of transverse shear moduli for composite honeycomb cores

Pareto based multi-objective optimization of turbulent heat transfer flow in helically corrugated tubes

Evolutionary algorithms have been widely used to tackle multi-objective optimization problems. Despite many successful applications there is a lack of theoretical knowledge on how and why this type of algorithm works particularly well for multi-objective problems. This special issue presents current advances to the theoretical understanding of evolutionary algorithms for multi-objective optimization. It comprises four very high quality papers which have been selected via a rigorous reviewing process. The first paper “On the Effect of Populations in Evolutionary Multi-Objective Optimisation” by Oliver Giel and Per Kristian Lehre studies the basic question whether a population is provably useful to compute the whole Pareto front of a multi-objective optimization problem. They compare restart strategies of several single-objective approaches based on one individual to a natural multi-objective approach using a population. Their analyses point out the benefits of the multi-objective evolutionary algorithm in a rigorous way. Using illustrative example functions they provide not only proven results but also a good intuition about why population-based approaches can outperform individual-based approaches in multi-objective optimization. The paper “Exploring the Runtime of an Evolutionary Algorithm for the Multi-Objective Shortest Path Problem” by Christian Horoba examines the behavior of evolutionary algorithms for the classical NP-hard multi-objective shortest path problem. The author considers an evolutionary approach based on -dominance and proves that this algorithm constitutes a fully polynomial-time randomized approximation scheme (FPRAS) for the problem. The algorithm achieves this by mimicking a problemspecific algorithm for that problem. This provides general insight into why evolutionary algorithms can perform well for problems that allow for efficient problem-specific algorithms. Hypervolume-based algorithms have become very popular in evolutionary multiobjective optimization. In the paper, “An Efficient Algorithm for Computing Hypervolume Contributions,” Karl Bringmann and Tobias Friedrich present a faster algorithm for determining the individual with the smallest hypervolume contribution in a given population. Furthermore, they study the problem of removing λ > 1 individuals with the smallest hypervolume contribution from a given population and present an elegant and fast method to carry out this task.

Single-objective continuous optimization can be challenging, especially when dealing with multimodal problems. This work sheds light on the effects that multi-objective optimization may have in the single-objective space. For this purpose, we examine the inner mechanisms of the recently developed sophisticated local search procedure SOMOGSA. This method solves multimodal single-objective continuous optimization problems based on first expanding the problem with an additional objective (e.g., a sphere function) to the bi-objective domain and subsequently exploiting local structures of the resulting landscapes. Our study particularly focuses on the sensitivity of this multiobjectivization approach w.r.t. (1) the parametrization of the artificial second objective, as well as (2) the position of the initial starting points in the search space. As SOMOGSA is a modular framework for encapsulating local search, we integrate Nelder–Mead local search as optimizer in the respective module and compare the performance of the resulting hybrid local search to its original single-objective counterpart. We show that the SOMOGSA framework can significantly boost local search by multiobjectivization. Hence, combined with more sophisticated local search and metaheuristics, this may help solve highly multimodal optimization problems in the future.

Green banking's potential to foster green economies and ensure a sustainable future is profound, emphasizing the critical importance of green finance in advancing SDGs and mitigating global environmental challenges. Central Asian banks, including India, have increasingly embraced green finance, acknowledging its pivotal role in addressing climate risks. The study has proposed a conceptual framework that depicts the interplay between drivers, facilitators, and accelerators of green banking and sustainable development goals (SDGs), especially SDG 7 (affordable and clean energy), SDG 11 (sustainable cities and communities), SDG 12 (responsible consumption and production) and SDG 13 (climate action), highlighting collaborative efforts needed for global sustainability. This article sheds light on the transformational potential of green banking projects by discussing both the practical and social aspects of the proposed initiatives.