Shouyong Jiang

University of Aberdeen | ABDN · Department of Computing Science

Decomposition of a complex multi-objective optimisation problem (MOP) to multiple simple subMOPs, known as M2M for short, is an effective approach to multi-objective optimisation. However, M2M facilitates little communication/collaboration between subMOPs, which limits its use in complex optimisation scenarios. This paper extends the M2M framework...

Brain storm optimization (BSO) is a population‐based intelligence algorithm for optimization problems, which has attracted researchers' growing attention due to its simplicity and efficiency. An improved BSO, called CIBSO, is presented in this article. First of all, a new grouping method, in which the population is partitioned into chunks according...

Dynamic multi-objective optimisation (DMO) handles optimisation problems with multiple (often conflicting) objectives in varying environments. Such problems pose various challenges to evolutionary algorithms, which have popularly been used to solve complex optimisation problems, due to their dynamic nature and resource restrictions in changing envi...

This study proposes a novel and lightweight bio-inspired computation technique named biological survival optimizer (BSO), which simulates the escape behavior of prey in the natural environment. This algorithm consists of two important courses, escape phase and adjustment phase. Specifically, in the escape phase, each search agent is required to upd...

In recent years, researchers have made significant progress in handling dynamic multi-objective optimization problems (DMOPs), particularly for environmental changes with predictable characteristics. However, little attention has been paid to DMOPs with stochastic changes. It may be difficult for existing dynamic multi-objective evolutionary algori...

Recently, dynamic multi-objective optimization has received growing attention due to its popularity in real-world applications. Inspired by polynomial fitting, this paper proposes a polynomial fitting-based prediction algorithm (PFPA) and incorporates it into the model-based multi-objective estimation of distribution algorithm (RM-MEDA) for solving...

This study proposes a novel and lightweight bio-inspired computation technique named biological survival optimizer (BSO), which simulates the escape behavior of prey in the natural environment. This algorithm consists of two important courses, escape phase and adjustment phase. Specifically, in the escape phase, each search agent is required to upd...

This study proposes a novel and lightweight bio-inspired computation technique named biological survival optimizer (BSO), which simulates the escape behavior of prey in the natural environment. This algorithm consists of two important courses, escape phase and adjustment phase. Specifically, in the escape phase, each search agent is required to upd...

Computational tools have been widely adopted for strain optimization in metabolic engineering, contributing to numerous success stories of producing industrially relevant biochemicals. However, most of these tools focus on single metabolic intervention strategies (either gene/reaction knockout or amplification alone) and rely on hypothetical optima...

Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly growing area of investigation. EDMO employs evolutionary approaches to handle multi-objective optimisation problems that have time-varying changes in objective functions, constraints and/or environmental parameters. Due to the simultaneous presence of dynamic...

In this article, we propose an evolutionary algorithm based on layered prediction (LP) and subspace-based diversity maintenance (SDM) for handling dynamic multiobjective optimization (DMO) environments. The LP strategy takes into account different levels of progress by different individuals in evolution and historical information to predict the pop...

Computational tools have been widely adopted for strain optimisation in metabolic engineering, contributing to numerous success stories of producing industrially relevant biochemicals. However, most of these tools focus on single metabolic intervention strategies (either gene/reaction knockout or amplification alone) and rely on hypothetical optima...

The reduced Tomgro model is one of the popular biophysical models, which can reflect the actual growth process and model the yields of tomato-based on environmental parameters in a greenhouse. It is commonly integrated with the greenhouse environmental control system for optimally controlling environmental parameters to maximize the tomato growth/y...

A mixed-integer programming (MIP) problem contains both constraints and integer restrictions. Integer restrictions divide the feasible region defined by constraints into multiple discontinuous feasible parts. In particular, the number of discontinuous feasible parts will drastically increase with the increase of the number of integer decision varia...

This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-based reaction mechanism aims to generate high-qua...

Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield. In order to optimally control greenhouses’ environmental parameters, one indispensable requirement is to accurately predict crop yields based on given environmental parameter s...

Most of the existing dynamic multi-objective evolutionary algorithms (DMOEAs) are effective, which focuses on searching for the approximation of Pareto-optimal front (POF) with well-distributed in handling dynamic multi-objective optimization problems (DMOPs). Nevertheless, in real-world scenarios, the decision maker (DM) may be only interested in...

After decades of effort, evolutionary algorithms have been able to solve a variety of multiobjective optimisation problems with diverse characteristics. However, the presence of irregularity in the Pareto-optimal front is increasingly recognised as a big challenge to some well-established algorithms. In order to further our understanding of this ir...

Multi-step-ahead prediction is considered of major significance for time series analysis in many real life problems. Existing methods mainly focus on one-step-ahead forecasting, since multiple step forecasting generally fails due to accumulation of prediction errors. This paper presents a novel approach for predicting plant growth in agriculture, f...

Huntington's disease (HD), a genetically determined neurodegenerative disease, is positively correlated with eye movement abnormalities in decision making. The antisaccade conflict paradigm has been widely used to study response inhibition in eye movements, and reliable performance deficits in HD subjects have been observed, including a greater num...

Multi-step prediction is considered of major significance for time series analysis in many real life problems. Existing methods mainly focus on one-step-ahead forecasting, since multiple step forecasting generally fails due to accumulation of prediction errors. This paper presents a novel approach for predicting plant growth in agriculture, focusin...

for archive only, looking at impact factor is not the best way to select journals, but could help to avoid low-quality journals

Differential evolution (DE) has been a popular algorithm for its simple structure and few control parameters. However, there are some open issues in DE regrading its mutation strategies. An interesting one is how to balance the exploration and exploitation behaviour when performing mutation, and this has attracted a growing number of research inter...

Motivation: Flux balance analysis (FBA) based bilevel optimisation has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimisation problems. However, most of them are of limited use due to biased optimality principle, p...

Population-based evolutionary algorithms have great potential to handle multiobjective optimisation problems. However, the performance of these algorithms depends largely on problem characteristics. There is a need to improve these algorithms for wide applicability. References, often specified by the decision maker’s preference in different forms,...

Population-based evolutionary algorithms have great potential to handle multiobjective optimisation problems. However, these algorithms depends largely on problem characteristics, and there is a need to improve their performance for a wider range of problems. References, which are often specified by the decision maker's preference in different form...

Flux balance analysis (FBA) based bilevel optimisation has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimisation problems. However, most of them are of limited use due to biased optimality principle, poor scalabili...

Many real-world applications can be modelled as dynamic constrained optimization problems (DCOPs). Due to the fact that objective function and/or constraints change over time, solving DCOPs is a challenging task. Although solving DCOPs by evolutionary algorithms has attracted increasing interest in the community of evolutionary computation, the des...

This paper proposes a new prediction-based dynamic multi-objective optimization (PBDMO) method, which combines a new prediction-based reaction mechanism and a popular regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for solving dynamic multi-objective optimization problems. Whenever a change is detected, PBDMO r...

Dynamic multiobjective optimization (DMO) has gained increasing attention in recent years. Test problems are of great importance in order to facilitate the development of advanced algorithms that can handle dynamic environments well. However, many of the existing dynamic multiobjective test problems have not been rigorously constructed and analyzed...

Computational modelling has demonstrated its great ability to predict phenotypes from genotypes and ease metabolic engineering tasks. This paper proposes a new strain design framework to identify genetic manipulations leading to high production of compounds of interest. The framework formulates strain design as a bilevel multiobjective optimisation...

Computer-aided techniques have been widely applied to analyse the biological circuits of microorganisms and facilitate rational modification of metabolic networks for strain design in order to maximise the production of desired biochemicals for metabolic engineering. Most existing computational methods for strain design formulate the network redesi...

Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, dynamic mul-tiobjective problems (DMOPs) have been increasingly studied in recent years. Whilst most studies concentrated o...

Multiobjective optimisation in dynamic environments is challenging due to the presence of dynamics in the problems in question. Whilst much progress has been made in benchmarks and algorithm design for dynamic multiobjective optimisation, there is a lack of work on the detectability of environmental changes and how this affects the performance of e...

Decomposition-based multiobjective evolutionary algorithms have received increasing research interests due to their high performance for solving multiobjective optimization problems. However, scalarizing functions, which play a crucial role in balancing diversity and convergence in these kinds of algorithms, have not been fully investigated. This p...

Particle swarm optimization is a population-based global search method, and known to suffer from premature convergence prior to discovering the true global minimizer for global optimization problems. Taking balance of local intensive exploitation and global exploration into account, a novel algorithm is presented in the paper, called dynamic cluste...

It has been increasingly reported that the multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) is promising for handling multiobjective optimization problems (MOPs). MOEA/D employs scalarizing functions to convert an MOP into a number of single-objective subproblems. Among them, penalty boundary intersection (PBI) is...

While Pareto-based multi-objective optimization algorithms continue to show effectiveness for a wide range of practical problems that involve mostly two or three objectives, their limited application for many-objective problems, due to the increasing proportion of nondominated solutions and the lack of sufficient selection pressure, has also been g...

This paper presents a new algorithm, called steady-state and generational evolutionary algorithm, which combines the fast and steadily tracking ability of steady-state algorithms and good diversity preservation of generational algorithms, for handling dynamic multiobjective optimization. Unlike most existing approaches for dynamic multiobjective op...

Dynamic multi-objective optimization has received growing research interest in recent years since many real-world optimization problems appear to not only have multiple objectives that conflict with each other but also change over time. The time-varying characteristics of these dynamic multi-objective optimization problems pose a new challenge to e...

In multiobjective optimization, a good quality indicator is of great importance to the performance assessment of algorithms. This paper investigates the effectiveness of the widely-used hypervolume indicator, which is the only one found so far to strictly comply with the Pareto dominance. While hypervolume is of undisputed success to assess the qua...

Convergence and diversity are two main goals in multiobjective optimization. In literature, most existing multiobjective optimization evolutionary algorithms (MOEAs) adopt a convergence-first-and-diversity-second environmental selection which prefers nondominated solutions to dominated ones, as is the case with the popular nondominated sorting base...

Convergence and diversity are two main goals in multiobjective optimization. In literature, most existing multiobjective optimization evolutionary algorithms (MOEAs) adopt a convergence-first-and-diversity-second environmental selection which prefers nondominated solutions to dominated ones, as is the case with the popular nondominated sorting base...

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a number of single-objective problems and solves them collaboratively. Since its introduction, MOEA/D has gained increasing research interest and has become a benchmark for validating new designed algorithms. Despite that,...

The multi-objective evolutionary algorithm based on decomposition (MOEA/D) has shown to be very efficient in solving multi-objective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantl...

The main goal of multiobjective optimization is to achieve a set of well-converged and evenly-distributed Pareto optimal points. While evolutionary algorithms have been reported to converge well, their distribution performance might not be as uniform as we expected, especially when the problems to be optimized involve complex Pareto fronts. In this...

Strength Pareto Evolutionary Algorithm 2 (SPEA2) has achieved great success for handling multiobjective optimization problems. However, it has been widely reported that SPEA2 gets subjected to a huge amount of computational effort while pursuing a good distribution of approximated solutions. This paper explores a new way to keep the good properties...

Dynamic multi-objective optimization has received increasing attention in recent years. One of striking issues in this field is the lack of standard test suites to determine whether an algorithm is capable of solving dynamic multi-objective optimization problems (DMOPs). So far, a large proportion of test functions commonly used in the literature h...

Many real-world optimization problems appear to not only have multiple objectives that conflict each other but also change over time. They are dynamic multi-objective optimization problems (DMOPs) and the corresponding field is called dynamic multi-objective optimization (DMO), which has gained growing attention in recent years. However, one main i...

In order to improve the local and global search ability of cuckoo search algorithm(CS) and its convergence rate,a new self-adaptive cuckoo search algorithm was proposed．In this algorithm,a self-adaptive parameter control strategy is used to adjust the step size of CS,thereby enhancing the search ability of CS．In addition,a mutation technique which...

Quantum-behaved particle swarm optimization (QPSO) has shown to be an effective algorithm for solving global optimization problems that are of high complexity. This paper presents a new QPSO algorithm, denoted LI-QPSO, which employs a model-based linear interpolation method to strengthen the local search ability and improve the precision and conver...