Chi Keong Goh

An evolutionary artificial immune system for multi-objective optimization

In this paper, an evolutionary artificial immune system for multi-objective optimization which combines the global search ability of evolutionary algorithms and immune learning of artificial immune systems is proposed. A new selection strategy is developed based upon the concept of clonal selection principle to maintain the balance between exploration and exploitation. In order to maintain a diverse repertoire of antibodies, an information-theoretic based density preservation mechanism is also presented. In addition, the performances of various multi-objective evolutionary algorithms as well as the effectiveness of the proposed features are examined based upon seven benchmark problems characterized by different difficulties in local optimality, non-uniformity, discontinuity, non-convexity, high-dimensionality and constraints. The comparative study shows the effectiveness of the proposed algorithm, which produces solution sets that are highly competitive in terms of convergence, diversity and distribution. Investigations also demonstrate the contribution and robustness of the proposed features.

On solving multiobjective bin packing problems using evolutionary particle swarm optimization

The bin packing problem is widely found in applications such as loading of tractor trailer trucks, cargo airplanes and ships, where a balanced load provides better fuel efficiency and safer ride. In these applications, there are often conflicting criteria to be satisfied, i.e., to minimize the bins used and to balance the load of each bin, subject to a number of practical constraints. Unlike existing studies that only consider the issue of minimum bins, a multiobjective two-dimensional mathematical model for bin packing problems with multiple constraints (MOBPP-2D) is formulated in this paper. To solve MOBPP-2D problems, a multiobjective evolutionary particle swarm optimization algorithm (MOEPSO) is proposed. Without the need of combining both objectives into a composite scalar weighting function, MOEPSO incorporates the concept of Paretos optimality to evolve a family of solutions along the trade-off surface. Extensive numerical investigations are performed on various test instances, and their performances are compared both quantitatively and statistically with other optimization methods to illustrate the effectiveness and efficiency of MOEPSO in solving multiobjective bin packing problems.

Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization

Although recent studies have shown that evolutionary algorithms are effective tools for solving multi-objective optimization problems, their performances are often bottlenecked by the suitability of the evolutionary operators with respect to the optimization problem at hand and their corresponding parametric settings. To adapt the search dynamic of evolutionary operation in multi-objective optimization, this paper proposes an adaptive variation operator that exploits the chromosomal structure of binary representation and synergizes the function of crossover and mutation. The overall search ability is deterministically tuned online to maintain a balance between extensive exploration and local fine-tuning at different stages of the evolutionary search. Also, the coordination between the two variation operators is achieved by means of an adaptive control that ensures an efficient exchange of information between the different chromosomal sub-structures throughout the evolutionary search. Extensive comparative studies with several representative variation operators are performed on different benchmark problems and significant algorithmic performance improvements in terms of proximity, uniformity and diversity are obtained with the incorporation of the proposed adaptive variation operator into the evolutionary multi-objective optimization process.

Evolving better population distribution and exploration in evolutionary multi-objective optimization

The aim of multi-objective evolutionary optimization is to minimize the distance between the solution set and the true Pareto front, to distribute the solutions evenly and to maximize the spread of solution set. This paper addresses these issues by presenting two features that enhance the optimization ability of multi-objective evolutionary algorithms. The first feature is a variant of the mutation operator that adapts the mutation rate along the evolution process to maintain a balance between the introduction of diversity and local fine-tuning. In addition, this adaptive mutation operator adopts a new approach to strike a compromise between the preservation and disruption of genetic information. The second feature is an enhanced exploration strategy that encourages the exploration towards less populated areas and hence achieves better discovery of gaps in the generated front. The strategy also preserves non-dominated solutions in the evolving population to achieve a good convergence for the optimization. Comparative studies of some well-known diversity operators, mutation operators and multi-objective evolutionary algorithms are performed on different benchmark problems, which illustrate the effectiveness and efficiency of the proposed features.

Modeling Civil Violence: An Evolutionary Multi-Agent, Game Theoretic Approach

This paper focuses on the design and development of a spatial evolutionary multi-agent social network (EMAS) to investigate the underlying emergent macroscopic behavioral dynamics of civil violence, as a result of the microscopic local movement and game-theoretic interactions between multiple goal-oriented agents. Agents are modeled from multi-disciplinary perspectives and their behavioral strategies are evolved over time via collective co-evolution and independent learning. Experimental results reveal the onset of fascinating global emergent phenomenon as well as interesting patterns of group movement and behavioral development. Analysis of the results provides new insights into the intricate behavioral dynamics that arises in civil upheavals. Collectively, EMAS serves as a vehicle to facilitate the behavioral development of autonomous agents as well as a platform to verify the effectiveness of various violence management policies which is paramount to the mitigation of casualties.

On Solving Multiobjective Bin Packing Problems Using Particle Swarm Optimization

The bin packing problem is widely found in applications such as loading of tractor trailer trucks, cargo airplanes and ships, where a balanced load provides better fuel efficiency and safer ride. In these applications, there are often conflicting criteria to be satisfied, i.e., to minimize the bins used and to balance the load of each bin, subject to a number of practical constraints. Unlike existing studies that consider only the minimization of bins, a two-objective mathematical model for the bin packing problem with multiple constraints is formulated in this paper. Without the need of combining both objectives into a composite scalar, a hybrid multiobjective particle swarm optimization algorithm (HMOPSO) incorporating the concept of Paretos optimality to evolve a family of solutions along the trade-off is proposed. The algorithm is also featured with bin packing heuristic, variable length representation, and specialized mutation operator to solve the multiobjective and multi-model combinatorial bin packing problem. Extensive simulations are performed on various test instances, and their performances are compared both quantitatively and statistically with other optimization methods. Each of the proposed features is also explicitly examined to illustrate their usefulness in solving the multiobjective bin packing problem.

Evolution and incremental learning in the iterative prisoner's dilemma

This paper investigates the use of evolution and incremental learning to find an optimal strategy in the iterative prisoners dilemma (IPD) problem, given an environment with a collection of unknown strategies. The Meta-Lamarckian Memetic learning (MLML) scheme is conceptualized based on the biological evolution of man and his abilities to accumulate knowledge and learn from past experiences. Learning was found to be the dominant force for improvement in the short run while improvement in the long run is sustained by the process of evolution. Learning is also much more effective when carried out on an incremental basis as the games progress. A series of simulation results obtained verified that the best performance is attained when a hybrid combination of learning and evolution is carried out on an incremental basis, not just evolution or learning alone.

Noise Handling in Evolutionary Multi-Objective Optimization

In addition to the need to satisfy several competing objectives, many real-world applications are also characterized by noise. In this paper, three noise-handling features, an experiential learning directed perturbation (ELDP) operator, a gene adaptation selection strategy (GASS) and a possibilistic archiving model are proposed. The ELDP adapts the magnitude and direction of variation according to past experiences for fast convergence while the GASS improves the evolutionary search in escaping from premature convergence in both noiseless and noisy environments. The possibilistic archiving model is based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition, the performances of various multi-objective evolutionary algorithms in noisy environments as well as the robustness and effectiveness of the proposed features are examined based upon three benchmark problems characterized by different difficulties.

Evolution and Incremental Learning in the Iterated Prisoner's Dilemma

This paper examines the comparative performance and adaptability of evolutionary, learning, and memetic strategies to different environment settings in the iterated prisoners dilemma (IPD). A memetic adaptation framework is developed for IPD strategies to exploit the complementary features of evolution and learning. In the paradigm, learning serves as a form of directed search to guide evolving strategies to attain eventual convergence towards good strategy traits, while evolution helps to minimize disparity in performance among learning strategies. Furthermore, a double-loop incremental learning scheme (ILS) that incorporates a classification component, probabilistic update of strategies and a feedback learning mechanism is proposed and incorporated into the evolutionary process. A series of simulation results verify that the two techniques, when employed together, are able to complement each others strengths and compensate for each others weaknesses, leading to the formation of strategies that will adapt and thrive well in complex, dynamic environments.

An asynchronous recurrent linear threshold network approach to solving the traveling salesman problem

In this paper, an approach to solving the classical Traveling Salesman Problem (TSP) using a recurrent network of linear threshold (LT) neurons is proposed. It maps the classical TSP onto a single-layered recurrent neural network by embedding the constraints of the problem directly into the dynamics of the network. The proposed method differs from the classical Hopfield network in the update of state dynamics as well as the use of network activation function. Furthermore, parameter settings for the proposed network are obtained using a genetic algorithm, which ensure a stable convergence of the network for different problems. Simulation results illustrate that the proposed network performs better than the classical Hopfield network for optimization.